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Developmental Patterns of Positive Affect Among Preadolescents in Primary School: A Two-Year Latent Growth Analysis of Peer, Teacher, and Family Influences
Diese Studie untersucht die Entwicklung positiver Affekte (PA) bei Heranwachsenden und konzentriert sich dabei auf die Einflüsse von Gleichaltrigen, Lehrern und Familien über einen Zeitraum von zwei Jahren. Die Forschungsergebnisse zeigen, dass die PA insgesamt stabil bleibt, aber signifikante Unterschiede zwischen den Individuen in der Entwicklung der PA beobachtet werden. Peer-Beziehungen und Familienzufriedenheit sind starke Prädiktoren für das Ausgangsniveau der PA, während die Sensibilität der Lehrer eine entscheidende Rolle bei Veränderungen der PA im Laufe der Zeit spielt. Die Studie untersucht auch geschlechtsspezifische Unterschiede und findet keine signifikanten Auswirkungen auf die Entwicklung der PA. Praktische Implikationen unterstreichen die Bedeutung eines unterstützenden Umfelds für die Aufrechterhaltung positiver emotionaler Erfahrungen in dieser kritischen Entwicklungsphase. Die Ergebnisse liefern wertvolle Erkenntnisse für die Gestaltung von Interventionen, die darauf abzielen, die PA und das emotionale Wohlbefinden von Schulkindern zu verbessern.
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Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
Positive affect (PA) in children tends to decline around the age of 10, with peers, teachers, and family being important socio-contextual factors influencing this development. However, no longitudinal studies have examined how these factors collectively shape PA of preadolescents (in ages 9–12) over time. The aim of this study was to explore the development of preadolescents’ PA and the extent to which peer relationships, teacher sensitivity, and family satisfaction predict PA, as well as potential gender differences. We followed 300 preadolescents from grades 4 to 5 across four timepoints. Socio-contextual factors had distinct effects on PA, with family satisfaction and peer relationships predicting initial PA levels, and peer relationships and teacher sensitivity influencing changes over time. No significant gender differences in PA were found. These findings have implications for the design of interventions to support and enhance PA and broader subjective well-being among primary school preadolescents.
Patrik Söderberg and Anna K. Forsman contributed equally to this work.
In this article, the anonymized version of the manuscript was published mistakenly. Hence, the word ‘anonymized’ was incorrectly appearing throughout the text and the incorrect supplementary information file with track changes was published online. The correct version is provided in this correction.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Positive affect (PA) refers to the extent to which an individual experiences positive emotions such as joy, interest, and alertness (Watson, 2002). It constitutes one of the three components of Diener’s (1984) subjective well-being model, alongside negative affect and life satisfaction. PA has been associated with several individual and environmental correlates, such as social support (Tian et al., 2013), as well as personality and emotional stability (Vittersø, 2001). Moreover, PA has been linked to a range of adaptive outcomes, including sociability, health (Lyubomirsky et al., 2005), student engagement (Reschly et al., 2008), and academic success (Nickerson et al., 2011). In school settings, PA is therefore recognized as an important objective for promoting well-being and related positive outcomes among young people (European Commission, 2024; Kleinkorres et al., 2020; Oberle, 2018; Thoilliez, 2011). However, while PA has been extensively studied in adulthood, and increasingly during adolescence, far less is known about its development during preadolescence (also known as early adolescence) (González-Carrasco et al., 2017; Izzo et al., 2022; Martin-Krumm et al., 2018)—a period corresponding to the later primary school years. Preadolescence represents a critical period for emotional development during which PA and well-being are strongly shaped by their immediate social environment (Griffith et al., 2021b; Ramsey & Gentzler, 2015; Rubin et al., 2006), particularly through interactions with peers, teachers, and family (Herres et al., 2018; Kiuru et al., 2016; Valcke et al., 2022). Given the dynamic nature of emotional development in youth, longitudinal research is essential for determining how PA changes over time (e.g., Baiocco et al., 2019; Izzo et al., 2022; Joing et al., 2020). However, although an increasing number of studies have employed longitudinal designs to examine PA in preadolescents (Casas & González-Carrasco, 2020; González-Carrasco et al., 2017; Izzo et al., 2022), there remains a considerable gap in research that specifically accounts for the socio-contextual influences of peers, teachers, and family on PA over time (Baiocco et al., 2019; Fino et al., 2025; Mínguez, 2020). Findings on gender differences in PA among preadolescents are also mixed (Bacter et al., 2021; Casas & González-Carrasco, 2019) with limited attention to how such differences unfold over time. Understanding the early developmental dynamics between PA and these key socio-contextual influences can deepen our understanding of preadolescent’s PA trajectories, providing valuable contributions to research on well-being across the lifespan and positive psychology. Therefore, this research aims to examine the development of PA in preadolescents, focusing on the roles of peers, teachers, and family, as well as potential gender differences in PA.
1 Developmental Patterns of Positive Affect in Preadolescents
During childhood and adolescence, neurological, physiological, cognitive, and social changes contribute to normative shifts in young people’s affective experiences (Coe-Odess et al., 2019). Preadolescence, in particular, often involves transitions such as adapting to higher academic demands, forming more complex peer relationships, and developing greater emotional autonomy (Rubin et al., 2006), making it a particularly important window for interventions aimed at supporting well-being. Notably, PA shows varied developmental patterns during this stage, with studies reporting both stability and decline (Feraco & Cona, 2024; Griffith et al., 2021a; Larson et al., 1996, 2002). While some research suggests that PA tends to increase through early childhood, with high levels of PA through age 9 (Olino et al., 2011; Thoilliez, 2011), a general decline is observed during preadolescence.
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For example, a recent large-scale longitudinal study by Feraco and Cona (2024) observed a decline in PA every six months across six measurement occasions in a sample of over 10,000 U.S. participants aged 9 to 13, noting relatively high PA levels at the start of the measurement period followed by a gradual decline. Similarly, Griffith et al. (2021a) found a linear decrease in PA over three years in a longitudinal study of 665 youth aged 9 to 17. Complementing these findings, cross-sectional studies have consistently identified a downward trend in PA, often occurring between the ages of 10 to 12 (Bacter et al., 2021; Esteban-Gonzalo et al., 2020; Martin-Krumm et al., 2018).
Intensive longitudinal studies further support the notion that preadolescence marks the onset of a downward trend in PA development. For example, Larson et al. (1996, 2002) found that PA not only becomes less stable but also less positive throughout childhood and preadolescence (Larson et al., 1996; Larson et al., 2002). Additionally, while Zurbriggen et al. (2021) suggests that PA fluctuations increase toward the end of preadolescence, Larson et al. (2002) found that these fluctuations gradually slow during adolescence. However, although intensive longitudinal studies offer valuable insights into short-term dynamics in state PA, it is essential to interpret such fluctuations in light of broader developmental trends in trait PA, given the interactive nature of trait and state affective processes (Kuppens et al., 2010). As such, mapping changes in trait PA during preadolescence remains crucial for understanding the foundations of (positive) affective development (Griffith et al., 2021a).
Collectively, these findings suggest that younger children typically exhibit relatively high levels of PA prior to the onset of decline. This pattern appears to hold across various cultural contexts, although the timing of the decline appears to vary between countries (Bacter et al., 2021; Casas & González-Carrasco, 2019). In most cases, the decline has been observed to begin around age 10 and appears to be unrelated to school transitions (Casas & González-Carrasco, 2019). Even though recent studies have provided valuable insights into the timing of PA decline in preadolescence, there has been a predominant focus on mean-level differences rather than developmental trajectories. While such cross-sectional studies are informative, understanding developmental trajectories requires methods that capture changes over time. Further research, particularly longitudinal studies, is therefore needed to refine these findings and to explore factors that might influence this trend during such a critical developmental period (see also Griffith & Hankin, 2025).
1.1 Influences of Peers, Teachers, and Family on Positive Affect During Preadolescence
The development of PA during preadolescence is influenced by socio-contextual factors, particularly interactions with peers, teachers, and family (e.g., Fino et al., 2025; Griffith et al., 2019b; Herres et al., 2018; Ramsey & Gentzler, 2015; Valcke et al., 2022). As children enter preadolescence, changes in the quality of these relationships play a central role in shaping their PA (Feraco & Cona, 2024; Fino et al., 2025; Mínguez, 2020). During this period, expanding social worlds and increased time spent outside parental supervision (McHale et al., 2003) make peers and teachers particularly pivotal to their PA (Griffith et al., 2021b; Oberle, 2018; Valcke et al., 2022).
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Recent large-scale cross-sectional research suggests that teacher factors, including teacher sensitivity and teacher-student relationships, significantly predict PA among children aged 10–12 years (Bacter et al., 2021; Mínguez, 2020). However, in a sample of 12,215 primary school students aged 9 to 12, Valcke et al. (2022) found that while teacher support contributes to PA, its influence is less pronounced compared to that of peers and family. Similarly, Fino et al. (2025) reported that teacher support did not contribute to PA of 2,339 preadolescents aged 9–13, whereas peer and family support exhibited significant positive effects.
Although preadolescents increasingly spend more time with peers (Lam et al., 2014), parental relationships continue to exert a substantial influence during this developmental stage (Moretti & Peled, 2004; Rubin et al., 2006). Numerous studies have highlighted the crucial role of family relationships in influencing preadolescent’s PA, as evidenced by both cross-sectional (Bacter et al. 2021; Hanzec Marković et al., 2022; Mínguez, 2020; Uusitalo-Malmivaara & Lehto, 2013) and longitudinal (Feraco & Cona, 2024) studies. For example, in a review of both cross-sectional and longitudinal studies, Izzo et al. (2022) found a positive correlation between family satisfaction and children’s PA, while also noting that parent-child conflict significantly predicts lower PA levels. Their findings emphasize that children’s PA is particularly influenced by the quality of close family relationships, particularly those with parents.
When examining the relative contributions of these socio-contextual factors (peers, teachers, and parents), most cross-sectional studies have found consistent associations with preadolescents’ PA. For instance, Valcke et al. (2022) found that all three sources of support were positively related with preadolescent’s PA. Among these, peer and parental support emerged as the strongest predictors of higher PA, while teacher support was found to have a weaker and less crucial role (Valcke et al., 2022). Similarly, Fino et al. (2025) found that peer support had the largest effect on children’s PA, followed by family support, while teacher support had no significant effect. In contrast, Bacter et al. (2021) found that neither the number of friends nor the extent of social relationships significantly influenced preadolescents’ PA. Instead, family and teacher-related factors emerged as key determinants at this developmental stage (Bacter et al., 2021).
To conclude, while peer and family relationships are generally found to contribute to preadolescent’s PA, the specific influence of teachers remains less clear, with some studies suggesting a weaker role compared to peers and family. These inconsistencies, together with the predominance of cross-sectional designs in the existing literature, highlight the need for further research, particularly longitudinal studies, to better understand how these key socio-contextual factors jointly shape children’s PA over time. Importantly, much of the previous work has focused primarily on mean-level differences, rather than on inter-individual developmental trajectories, which risks obscuring significant heterogeneity in how PA evolves during preadolescence.
1.2 Gender Differences in Positive Affect During Preadolescence
From a developmental perspective, it is reasonable to expect gender differences in PA during preadolescence, given differences in pubertal onset (Marceau et al., 2011) and girls’ heightened socioemotional sensitivity (Guyer et al., 2016), which reflects increased emotional awareness and support-seeking within changing socio-contextual factors (Rose & Rudolph, 2006). Still, existing research on PA among youth aged 8–12 has produced mixed findings, with some studies reporting higher PA levels in boys while others suggest minimal or context-dependent differences. For example, cross-sectional studies have found that gender differences in PA become more pronounced with age, particularly around age 12, with girls often experiencing a steeper decline in PA during preadolescence (Casas et al., 2020; Esteban-Gonzalo et al., 2020). Similarly, large-scale comparative studies have reported that boys generally exhibit higher PA than girls, although exceptions exist across different countries (Bacter et al., 2021; Casas et al., 2020; Mínguez, 2020).
However, some studies have reported little to no gender differences in PA among children aged 6–12, with findings suggesting that both boys and girls typically exhibit high levels of PA before adolescence (Thoilliez, 2011; Uusitalo-Malmivaara & Lehto, 2013). Interestingly, although girls often report higher perceived social support from teachers, peers, and parents than boys, this does not necessarily correspond to higher PA levels, suggesting that social support alone may not fully explain gender differences in PA (Valcke et al., 2022).
Longitudinal studies provide additional insights into developmental trends, indicating that boys and girls follow distinct PA trajectories over time. For example, Griffith et al. (2021a) found that while girls initially reported higher PA levels, they experienced a steady linear decline over time. In contrast, boys followed a quadratic trajectory, with PA increasing until around age 12 before decreasing. Similarly, Casas and Gonzales (2020) observed that boys’ PA generally increased over a five-year period, whereas girls’ PA tended to decrease. Conversely, Larson et al. (2002) found in their intensive longitudinal study that boys exhibited less favorable long-term patterns of daily PA compared to girls, based on data collected at two time points: initially in early adolescence (ages 10–14) and followed into middle to late adolescence (ages 13–18).
In sum, findings on gender differences in PA during preadolescence remain inconsistent, with some studies reporting higher PA in boys, others finding minimal differences, and longitudinal research indicating divergent developmental trajectories. The scarcity of longitudinal studies highlights the need for further research to better understand how gender differences in PA may emerge and change over time (see also Griffith & Hankin, 2025).
1.3 The Present Study
Previous research on PA has mainly centered on adults and adolescents, relying heavily on cross-sectional data with limited longitudinal studies available on preadolescents (e.g., Izzo et al., 2022). Although some studies have provided valuable insights into mean-level differences in PA during preadolescence, few have explored how individual trajectories unfold over time. While mean-level differences indicate whether a sample changes on average, developmental trajectories provide insights into how subgroups change, when these changes occur, and which factors predict them. However, there is a notable lack of longitudinal research examining how socio-contextual factors, such as peers, teachers, and family, collectively shape PA development (Fino et al., 2025; Mínguez, 2020), as well as inconsistencies in how PA evolves differently for boys and girls. Gaining a deeper understanding of these influences is crucial for detecting individual differences and informing timely, context-sensitive efforts to support preadolescents’ PA and broader subjective well-being (e.g., Baiocco et al., 2019). Therefore, this study aims to address this methodological gap by examining both mean-level differences and developmental trajectories of PA among preadolescents in grade 4 over a two-year period, as well as the roles of peer, teacher, and family factors, alongside potential gender differences. Specifically, we focus on baseline predictors to examine how initial levels of socio-contextual factors are associated with the developmental trajectories of PA, an approach commonly recommended in longitudinal research on individual differences (Curran et al., 2010). The following research questions and corresponding hypotheses guide the study:
(1) How does the development of PA in preadolescents progress from grade 4 to 5?
H1: We hypothesize that PA in preadolescents will show a general decline from grade 4 through grade 5 (Feraco & Cona, 2024; Griffith et al., 2021a).
(2) What are the roles of socio-contextual factors (peers, teachers, and family) in influencing the initial levels and changes over time in PA among preadolescents in grade 4 through 5?
Given the scarcity of longitudinal studies examining how peer, teacher, and family influences jointly affect PA in preadolescents, hypotheses regarding initial PA levels are primarily based on cross-sectional studies in which all three socio-contextual factors were assessed in the same model. Given that cross-sectional studies report mixed findings on the relative effects of peer, teacher, and family factors on initial PA (Fino et al., 2025; Valcke et al., 2022) and that longitudinal research on developmental trajectories is limited, both the relative effects on initial PA and the trajectories of PA are examined exploratorily.
H2a: Peer factors are expected to be positively associated with higher initial levels of PA (Fino et al., 2025; Valcke et al., 2022).
H2b: Teacher factors are expected to show a positive association with initial PA (Bacter et al., 2021; Valcke et al., 2022).
H2c: Family factors are expected to be positively related to initial PA levels, as evidenced by both longitudinal (Griffith & Hankin, 2025) and cross-sectional studies (Bacter et al., 2021; Fino et al., 2025; Valcke et al., 2022).
(3) Are there gender differences in the development of PA among preadolescents from grade 4 to 5?
Due to inconsistencies in prior findings and a lack of longitudinal research on gender differences in the development of PA during preadolescence, this question is approached exploratively.
2 Method
2.1 Participants and Procedure
The present study utilizes data collected through an accelerated longitudinal cohort design as part of the ongoing ‘CONSENSUS’ project at Åbo Akademi University (Nordmyr et al. 2023). A total of 13 Swedish-speaking primary schools in Finland were invited to participate in the survey at the start of the data collection. Specifically, we utilized data from pupils in grade 4 at T1 (autumn 2022) and followed them through T2 (spring 2023), T3 (autumn 2023), and T4 (spring 2024), covering their transition from grade 4 to 5 within the Finnish primary education system. To note, in the lower levels of basic education (grades 1–6), teaching is generally given by a designated class teacher (Paronen & Lappi, 2018). The baseline sample consisted of 300 pupils (n = 160 girls, 53.3%; n = 131 boys, 43.7%; n = 9 missing data, 3.0%), all of whom were pupils whose parents provided consent for their child’s participation in the research. Retention rates ranged from 74.7% to 79.3% across the four assessment points.
This study adheres to the ethical principles outlined by the Finnish National Board on Research Integrity, TENK (2019) for research with human participants and ethical review in the human sciences in Finland. The project received ethical approval from The Board for Research Ethics at Åbo Akademi University (FEN) in February 2022.
2.2 Measures
The specific timepoints used for each variable are detailed in the descriptions below. While item-level descriptions for all measures are presented in Table S1, the original sources for the instruments and any adaptation employed are described in Nordmyr et al. (2023).
2.2.1 Positive Affect
Positive affect (PA) was assessed using the six positively valenced items from the 12-item Scale of Positive and Negative Experience (SPANE; Diener et al., 2009), which asks pupils to rate how frequently they have experienced these feelings over the past four weeks (e.g., joyful, happy). Each item was rated on a 5-point Likert scale ranging from 1 (very seldom or never) to 5 (very often or always). Cronbach’s alpha for PA across T1-T4 ranged from α = .82 to α = .88. PA was measured at four time points (T1–T4), with each time point approximately six months apart.
2.2.2 Peer Relationships
Peer relationships was measured using four items covering social support and peers, adapted from the 27-item KIDSCREEN version (The KIDSCREEN Group Europe, 2006; Ravens-Sieberer et al., 2005; Ravens-Sieberer et al., 2007). An established Swedish translation of the instrument, provided by The KIDSCREEN Group Europe (2006), was used. The questions (e.g., “Have you had fun with your friends?”; “Have you and your friends helped each other?”) focus on experiences from the past week and were rated on a 5-point Likert scale ranging from 1 (never) to 5 (always), with good internal consistency at T1 (Cronbach’s α = .81).
2.2.3 Teacher Sensitivity
Teacher sensitivity was assessed using six items from the Teacher Sensitivity dimension of the Emotional Support Scale by Schenke et al. (2017). The Teacher Sensitivity sub-dimension is designed to assess pupils’ perceptions of the teacher’s responsiveness to their individual emotional and academic needs. The items (e.g., “Our teachers care about how we feel”; “We can count on our teachers for help when we need it”) were measured on a 5-point Likert scale ranging from 1 (not at all true) to 5 (very true), with good internal consistency at T1 (Cronbach’s alpha α = .88).
2.2.4 Family Satisfaction
The Family domain of the Multidimensional Students’ Life Satisfaction Scale (MSLSS; Huebner, 2001; Huebner et al., 1998) was used to assess family satisfaction. This domain encompasses seven items measuring respondents’ satisfaction with family members and the relationships among family members. The items (e.g., “I enjoy being at home with my family”; “I like spending time with my parents”) were measured on a 4-point Likert scale ranging from 1 (never) to 4 (almost always), demonstrating acceptable internal consistency at T1 (Cronbach’s alpha α = .78).
2.3 Data Analytic Plan
Preliminary analyses included descriptive statistics, correlations, missing data analysis, confirmatory factor analysis (CFA), and measurement invariance testing, which informed the main analyses using latent growth models (LGM). The analyses were conducted using IBM SPSS Statistics (Version 29) and MPlus (Version 8.8).
2.3.1 Missing Data Analysis
Little’s MCAR test was conducted to evaluate whether the missing data was Missing Completely at Random (MCAR). For PA across all four time points (T1-T4), the test indicated a good fit, χ2(829) = 814.82, p = .631, supporting the MCAR assumption. When adding the three socio-contextual variables (peer relationships, teacher sensitivity, and family satisfaction), the missing data also supported the MCAR assumption. For the main analyses conducted in MPlus, missing data were handled using full-information maximum likelihood (FIML; Enders, 2022). Additionally, the robust maximum likelihood estimator (MLR) was employed to handle non-normal data.
2.3.2 Confirmatory Factor Analyses
Prior to addressing our research questions, we tested the factor structure at each timepoint for our variables of interest (PA, peer relationships, teacher sensitivity, and family satisfaction) by running a series of CFAs. Given the slight non-normality in some items, we employed MLR to obtain robust standard errors and parameter estimates. Model fit was evaluated using the comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Good model fit was defined by CFI and TLI values ≥ .95, RMSEA values < .05, and SRMR ≤ .08 (Hu & Bentler, 1999), with RMSEA values between .06 and .10 indicating adequate fit (McCallum et al., 1996).
Although the χ2 statistics were statistically significant for most models (e.g., T1 Teacher Sensitivity: χ2(9) = 25.48, p < .001), other fit indices generally indicated good model fit across timepoints. Specifically, the CFI and TLI were high for most variables, and the RMSEA was below the threshold of .08 for most variables (PA and Peer Relationships). Family Satisfaction consistently exhibited the poorest fit across all timepoints. Peer relationships and PA generally exhibited good fit across timepoints, with minimal variation in fit indices. Results for each variable are presented in Table S2.
2.3.3 Measurement Invariance Testing
The measurement invariance of the construct PA was assessed through a series of longitudinal CFAs. First, a baseline model (configural invariance) was tested to examine whether the hypothesized factor structure held at each timepoint without imposing invariance constraints. Next, a metric model (factorial invariance) was tested by constraining factor loadings to be equal across the four timepoints. Finally, a scalar model (scalar invariance) was tested by constraining both factor loadings and indicator intercepts of the observed variables to equality across timepoints. Measurement invariance was determined using the criteria ΔCFI > -.01, along with ΔRMSEA < .01, to indicate full invariance in model comparisons (Lin et al., 2019).
The longitudinal CFA results for PA indicate that the measurement model demonstrates stability and consistency over time. Both the configural and metric invariance models show good fit, with CFI and TLI values above the recommended thresholds, suggesting that comparisons of latent factors and their means are valid across timepoints. The scalar invariance model also achieves an acceptable fit, indicating that the instrument functions similarly across timepoints, allowing for valid comparisons without concerns about time-specific measurement bias. The small difference in CFI between the scalar and metric models (ΔCFI = .006) is well below the cut-off of -.01, further supporting the adequacy of scalar invariance. Model fit for the longitudinal CFAs are presented in Table S3.
2.3.4 Latent Growth Models
The analysis of developmental patterns in PA over time was conducted using LGM within a structural equation modeling framework. LGM is particularly well-suited for examining inter-individual differences in both the initial level (intercept) and growth (slope) in longitudinal data, allowing for the modeling of individual trajectories of change and the investigation of predictors for these growth patterns (Curran et al., 2010; Grimm et al., 2011).
First, an unconditional LGM was applied, focusing exclusively on PA to establish a baseline trajectory across four measurement points over a two-year period (RQ1). Both linear and quadratic models were tested, with the optimal growth function determined based on CFI, TLI, RMSEA, and SRMR. Model selection was further supported by the Akaike information criterion (AIC) and Bayesian information criterion (BIC) (Kim et al., 2016), with lower values indicating a better model fit. Additionally, variance in the growth factors (intercept and slope) was examined, as significant variance indicates heterogeneity in the change of PA within the cohort. Although the quadratic model demonstrated slightly better fit statistics (see Table S4), the quadratic slope’s mean and variance were non-significant. Given this, the linear growth model was deemed more appropriate, as it provided a more parsimonious representation of PA change while maintaining statistically significant variance in both the intercept and slope (Curran et al., 2010).
Next, a conditional model was specified and estimated by incorporating socio-contextual factors at T1 (i.e., peer relationships, teacher sensitivity, and family satisfaction) to assess their combined effects on both the linear intercept and slope of PA over time (RQ2). Finally, gender was tested as a predictor in the unconditional model and as a moderator of peer, teacher, and family factors in the conditional model using a multi-group LGM to examine its influence on PA development (RQ3). The Satorra-Bentler scaled chi-square difference test (Satorra & Bentler, 2001) was conducted to evaluate whether gender as a moderator significantly impacted model fit. In both the unconditional and conditional models, factor loadings for the latent intercept were fixed at 1 across all four time points (T1–T4) to represent the initial level, while those for the slope were fixed at 0, 1, 2, and 3 to reflect the temporal progression.
3 Results
3.1 Preliminary Analyses
The descriptive statistics of PA across the four timepoints and for baseline (T1) predictors—Peer Relationships, Teacher Sensitivity, and Family Satisfaction—along with the correlations among these variables are presented in Tables 1 and 2. The data met acceptable normality criteria, with skewness values below 3 and kurtosis values below 4 (Kline, 2023), as shown in Table 1.
Table 1
Descriptive statistics for variables of interest (N = 300)
n
M
SD
Skewness
Kurtosis
Positive affect
T1
228
4.05
.67
−1.13
2.58
T2
235
4.10
.65
−.75
1.05
T3
238
4.04
.68
−.79
1.26
T4
224
3.95
.83
−.59
.70
Peer relationships
T1
217
4.15
.71
−1.56
3.43
Teacher sensitivity
T1
226
4.33
.72
−1.15
.94
Family satisfaction
T1
228
3.35
.47
−.76
.23
T1 = timepoint 1. T2 = timepoint 2. T3 = timepoint 3. T4 = timepoint 4. SD = standard deviation
Table 2
Zero-order correlation matrix
1
2
3
4
5
6
7
8
1. PA T1
–
2. PA T2
.28**
–
3. PA T3
.43**
.45**
–
4. PA T4
.24**
.42**
.46**
–
5. PR T1
.47**
.17*
.26**
.15
–
6. TS T1
.31*
.16*
.26**
.33**
.29**
–
7. FS T1
.54*
.29**
.40**
.37**
.39**
.24**
–
8. Gender
-.09
.03
.02
.04
−.12
.01
−.15*
–
N = 300. * = p < .05. ** = p < .01. PA = positive affect. PR = peer relationships. TS = teacher sensitivity. FS = family satisfaction. T1 = timepoint 1. T2 = timepoint 2. T3 = timepoint 3. T4 = timepoint 4
3.2 Main Analyses
3.2.1 Developmental Trajectory of Positive Affect
An unconditional linear LGM demonstrated good model fit, with an RMSEA value of .035 (90% CI: .000, .094) and a CFI value of .982 (see Table S5). In addressing research question 1, the results of the unconditional LGM revealed an estimated mean PA score at T1 (intercept) of 4.068 (SE = .039, p < .001), with significant variance (σi2 = .193, SE = .050, p < .001), indicating meaningful individual differences in the initial level. PA growth was statistically non-significant, with a mean slope of -.020 (SE = .050, p = .233), suggesting no overall change in PA from fall of grade 4 to spring of grade 5. However, the variance of the slope factor was statistically significant (σs2 = .024, SE = .011, p = .030), indicating variability in the trajectories of PA growth over time. The covariance between the intercept and slope latent variables was non-significant (Covi,s is = -.017, SE = .021, p = .419), indicating that preadolescent’s initial levels of PA were not associated with the linear change over time.
3.2.2 Effects of Peer Relationships, Teacher Sensitivity, and Family Satisfaction on Positive Affect
A conditional linear LGM with predictors demonstrated good model fit, although the SRMR was slightly above the acceptable range at .081 (see Table S5). In addressing research question 2, the predictors (peer relationships, teacher sensitivity, and family satisfaction at T1) showed varying effects on PA growth. Statistically significant predictors of the initial level of PA were Peer Relationships (β = .31, p = .004) and Family Satisfaction (β = .56, p < .001), but not Teacher Sensitivity (β = .15, p = .086). The significant predictors of the slope for PA were Peer Relationships (β = -.36, p < .001) and Teacher Sensitivity (β = .22, p = .044), but not Family Satisfaction (β = -.13, p = .277). Additional analyses testing each predictor separately confirmed the robustness of the negative association between peer relationships and PA slope. These supplementary results, including scatterplots illustrating the associations between variables at T1 and changes of PA over time, are provided in Table S6 and Figs. S1–S3. Additionally, results from the quadratic models (included for comparison) are presented in Table S7 to provide a more comprehensive view of the developmental trajectory of PA.
To note, standardized regression coefficients and their associated significance values are reported in the main text to facilitate interpretability and comparability. However, Table 3 presents both standardized and unstandardized parameter estimates, along with standard error and p-values derived from the unstandardized estimates for the conditional linear LGM. Fig. 1 depicts standardized regression coefficients for the specified conditional linear LGM.
Table 3
Parameter estimates from the conditional LGM
Predictors of PA growth
β
B
SE
p
Intercept PA predicted by
Peer relationships
.306
.207
.079
.008
Teacher sensitivity
.150
.102
.056
.067
Family satisfaction
.560
.565
.089
.000
Slope PA predicted by
Peer relationships
−.364
−.093
.035
.008
Teacher sensitivity
.224
.058
.031
.062
Family satisfaction
−.134
−.051
.047
.280
Covariance
Intercept with linear slope
−.036
.019
.060
PA = positive affect. β = standardized beta coefficient. B = unstandardized beta coefficient. SE = standard error of the unstandardized effect size
Fig. 1
Visualized conditional linear LGM illustrating standardized regression coefficients for the effects of peer relations, teacher sensitivity, and family satisfaction at T1 on PA intercept and slope. PA = positive affect. I = intercept (i.e., initial levels of PA at T1). S = slope (i.e., linear change of perceived PA over time). PR = peer relationships. TS = teacher sensitivity. FS = family satisfaction. T1 = timepoint 1. T2 = timepoint 2. T3 = timepoint 3. T4 = timepoint 4. 1 = the intercept is specified by setting its factor loadings at 1. 0, 1, 2, 3 = the factor loadings of the linear slope are fixed to the values reflecting the spacing of assessment over time. Single-headed bold lines represent significant regression path coefficients (p < .05). Single-headed dotted lines represent non-significant regression path coefficients (p > .05). Double-headed arrows represent covariance, with dotted arrows indicating non-significant covariance.
In addressing research question 3, gender was not a statistically significant predictor of either the initial level or slope of PA in the unconditional or conditional model. Including gender as a covariate did not significantly improve the models, as indicated by a Satorra-Bentler scaled chi-square difference test, with the inclusion of gender worsening model fit in the conditional model. Moreover, the effects of peer relationships, family satisfaction, and teacher sensitivity on PA development remained consistent, even when gender was tested as a moderator.
4 Discussion
The aim of this study was to examine the development of PA among preadolescents starting in 4th grade over two school years, as well as the influence of peers, teachers, and family on their PA, including an exploration of gender effects. Our key findings are three-fold. First, while overall PA remained stable over time, statistically significant inter-individual differences in preadolescent’s PA development were observed. Second, socio-contextual factors had distinct effects on PA, with peer relationships and family satisfaction significantly predicting initial PA levels, while peer relationships and teacher sensitivity were associated with changes in PA over time. Third, gender did not predict PA development, nor did it moderate the relationship between the predictors and PA.
4.1 Development and Stability of Positive Affect Among Preadolescents
Our findings revealed that while preadolescents in grade 4 initially reported relatively high levels of PA, the mean-level PA trajectory remained stable over the two-year period from fall 2022 to spring 2024. This pattern fails to support H1 and contrasts with most previous research, which has generally identified a decline in overall PA starting around age 10 (Bacter et al., 2021; Casas & González-Carrasco, 2019; Casas et al., 2020; Feraco & Cona, 2024; Griffith et al., 2021a). This discrepancy may be due to—or depend on—factors at both the micro level (e.g., family, school) and macro level (e.g., country, cultural context), which may interact with and shape preadolescent’s emotional development in different ways (Ramsey & Gentzler, 2015).
However, we found statistically significant inter-individual variability in PA trajectories, indicating that while most preadolescents maintained stable mean-level PA levels over time, some showed different patterns of change, with slight increases or decreases in PA across the study period. The observed variability in trajectories aligns with previous research suggesting heterogeneity in PA development during preadolescence (e.g., Feraco & Cona, 2024; Griffith et al., 2021a).
4.2 The Roles of Peer Relationships, Teacher Sensitivity, and Family Satisfaction in Predicting Positive Affect Trajectories
Socio-contextual factors played different roles in PA development. Regarding initial PA levels, peer relationships and family satisfaction were positively associated with PA, supporting the directional predictions of H2a and H2c. In contrast, H2b was not supported, as teacher sensitivity did not significantly predict initial PA, consistent with prior findings (Fino et al., 2025). For changes in PA over time, peer relationships and teacher sensitivity were significant predictors: peer relationships showed a small but significant negative effect on the slope, whereas teacher sensitivity was the only predictor with a positive significant effect on PA development. While family satisfaction had the strongest effect on initial PA, with a moderate-to-large positive association (β = .56), it was not significantly related to changes in PA over time. Instead, teacher sensitivity emerged as a significant predictor of changes in PA. These findings consist with previous research highlighting the growing impact of peers and teachers in preadolescence (Feraco & Cona, 2024; McHale et al., 2003).
The small but significant negative effect of peer relationships on PA development indicates that preadolescents with initially stronger peer support may experience a greater relative decrease in PA over time (see Fig. S1). Conversely, preadolescents with lower levels of peer relations at baseline may have benefited more from improvements in their social environment, leading to a steeper increase in PA. An alternative explanation is that as children grow older, social dynamics shift, and peer support, while important, may not be sufficient to maintain high PA levels when other factors (e.g., academic demands, biological influences, dispositional traits) come into play (Rubin et al., 2006).
Overall, these findings align with previous research on the role of social bonds in preadolescent’s PA, with most evidence coming from cross-sectional studies highlighting the influence of family and peer factors, while teachers appear to play a lesser role (e.g., Fino et al., 2025; Valcke et al., 2022). The limited longitudinal research available (e.g., Griffith et al., 2021b) suggests similar patterns. However, to our knowledge, no previous longitudinal study has examined the relative contributions of these three socio-contextual factors to preadolescents’ PA over time within a single model, making our study the first to present evidence on their simultaneous influence on PA trajectories in this age group. As such, definitive conclusions about the relative importance of these factors should be drawn with caution.
4.3 Gender Differences in Positive Affect Trajectories Among Preadolescents
While previous research has often reported higher PA levels in boys than in girls during preadolescence (e.g., Bacter et al., 2021; Valcke et al., 2022) and identified gender differences in PA trajectories (Griffith et al., 2021a), our study found no significant gender differences, either as a predictor of PA or in mean-level developmental trajectories between boys and girls. However, this finding aligns with a smaller body of cross-sectional (e.g., Casas et al., 2020; Thoilliez, 2011; Uusitalo-Malmivaara & Lehto, 2013) and longitudinal (e.g., Sallquist et al., 2009) studies suggesting no or minimal gender differences in PA among youth under 12. Thus, since our study followed preadolescents from grades 4 to 5, gender differences in PA may not yet have fully developed, supporting the notion that such disparities may emerge later in adolescence (Casas et al., 2020; Esteban-Gonzalo et al., 2020).
Cross-national research further illustrates considerable variation in PA trajectories and gender differences, with steeper declines and more pronounced gender disparities, particularly among girls in certain Western and East Asian countries (Bacter et al., 2021; Casas et al., 2020; Mínguez, 2020). For example, Casas et al. (2020) found that gender differences in PA are minimal among 10-year-olds but become more pronounced by age 12 in countries like South Korea, Germany, and the United Kingdom, where girls experience a sharper decline in PA compared to boys. However, in some countries, including Israel, Nepal, and Algeria, boys exhibited a slightly greater decrease in PA than girls (Bacter et al., 2021). Notably, while Bacter et al. (2021) identified higher PA levels among boys in Finland, Casas et al. (2020) found no significant gender differences in PA in Finland. These mixed findings highlight the complexity of gender differences in PA as well as a need for further research to explore factors that may contribute to these variations.
4.4 Strengths, Limitations, and Future Directions
Although the present study has several strengths, including a two-year longitudinal design that simultaneously examines the development of preadolescents’ PA trajectories and how these are influenced by three key socio-contextual factors (peers, teachers, and family), several limitations should be acknowledged. First, our sample is limited to Finnish preadolescents from a relatively culturally homogenous context, which may restrict the generalizability of our findings to more diverse or different cultural populations. Future research should therefore examine whether similar patterns emerge in more culturally diverse populations. Additionally, while our results did not align with most previous research showing declines in PA and gender differences, to the best of our knowledge, no prior longitudinal study has specifically examined PA development in Finnish primary school students around ages 9 to 12. Our findings suggest that PA trajectories are similar for girls and boys; however, there is a scarcity of longitudinal studies that have included gender as a predictor or as a moderator of PA development during preadolescence. To better understand these patterns and inconsistencies, future longitudinal studies should replicate or extend our findings. Additionally, although gender differences in the shape of PA trajectories (e.g., linear vs. quadratic) were not evaluated in the present study, previous work suggests this could be a relevant factor (Griffith et al., 2021a, 2021b). Future research could examine whether boys and girls differ in the form of PA development over time (see also Griffith & Hankin, 2025).
Second, the study’s accelerated longitudinal cohort design structured assessments according to the school semester rather than participants’ exact chronological ages, meaning that developmental changes were tracked relative to grade progression rather than individual age. Consequently, some variability in PA and socio-contextual factors may reflect age differences within the same grade cohort. Future studies could complement semester-based designs with age-based assessments to further explore developmental patterns (see also Griffith & Hankin, 2025). We also note the possibility of ceiling effects in PA, given the relatively high mean scores in our sample, which may have limited the upper range of responses, although significant individual differences in initial levels and trajectories were observed.
Third, as this study focuses on preadolescents, the role of teachers may not yet be as pronounced, whereas the impact of family tends to decline with age. This is consistent with findings from Tian et al. (2013), who found that among peer, teacher, and family support, only teacher support predicted PA among early adolescents aged 12–14. Future studies could examine whether these developmental patterns hold across different age groups. Third, our study measured PA using the SPANE (Diener et al., 2009), which demonstrated sufficient reliability and measurement invariance across timepoints in our study. However, previous cross-sectional and longitudinal research on PA in preadolescents has often used other instruments, such as the Positive and Negative Affect Schedule (PANAS; e.g., Griffith et al., 2021a, see Ramsey & Gentzler, 2015 for a review). Variation in measurement tools, including item content and time frames, may impact comparability across studies. Therefore, future research could examine whether findings hold across, for example, different PA measures (see also Backman-Nord et al., 2025). An additional limitation concerns the satisfaction aspects of the family satisfaction measure, which may overlap conceptually with PA, as both reflect aspects of subjective well-being. This conceptual overlap could partially explain the observed associations and should be considered when interpreting the findings.
Fourth, standardized scales were used for the socio-contextual measures, with internal consistency and CFAs indicating good reliability and measurement invariance across timepoints in the current sample. Nevertheless, as some measures were adapted and subsets of items were used, further validation in diverse samples is warranted to strengthen the generalizability of findings. Additionally, all data were collected via single-informant self-report questionnaires, which may introduce shared method variance and limit the multidimensional assessment of the constructs. Fifth, although the two-year time span offers valuable longitudinal insight into PA development during preadolescence, it may still be too limited to fully capture more complex or long-term developmental trajectories. A longer follow-up period could allow for a clearer and more robust modeling of growth patterns, especially given that affective development may evolve differently as children transition into adolescence, for example, during school transitions.
4.5 Practical Implications
Considering the significant variance in both the initial levels of PA and its growth trajectories, these findings highlight the importance of accounting for individual differences when designing future interventions, as some preadolescents may experience declines or slower growth in PA during the middle to later years of primary school. This is particularly important given that greater PA variability has been associated with negative mental health outcomes, such as depressive symptoms over time (e.g., Maciejewski et al., 2023). While family satisfaction had the strongest association with PA at baseline, its predictive value was not significant over time, whereas peer relationships remained important over the two school years. Teacher sensitivity, on the other hand, had the strongest impact on PA change over time. These findings emphasize the need for schools to prioritize both teacher-student and student-student relationships in promoting preadolescent’s positive emotional development in school settings, aligning with recommendations in positive psychology intervention studies (Laakso et al., 2021; Roth et al., 2017). Additionally, as family satisfaction was most strongly linked to PA in grade 4 students, strengthening school-home collaboration and promoting well-being in families, may further support preadolescent’s PA, particularly for those facing challenges at home or experiencing a decline in PA (e.g., Laakso et al., 2023).
Although our findings suggest that PA remained relatively stable across the two-year period, this pattern contrasts with previous research indicating a normative decline during preadolescence. Given that a large body of research indicates that PA tends to decline after ages 9–12 (e.g., Bacter et al., 2021; Griffith et al., 2021a; Larson et al., 2002), it is particularly important to foster supportive environments that help sustain positive emotional experiences during this critical developmental stage. Strengthening these protective factors early on may play a key role in mitigating the typical decline in PA observed during preadolescence.
5 Conclusion
This study contributes to the field, as no previous longitudinal research has examined the development of preadolescents’ PA while simultaneously accounting for the socio-contextual influences of peers, teachers, and family. Among the key findings made were that preadolescents’ PA remained stable from grade 4 to grade 5, with statistically significant inter-individual differences observed. Moreover, the socio-contextual factors influenced PA in distinct ways. Family satisfaction was the strongest predictor at the initial time point, but it did not predict changes in PA over time. In contrast, peer relationships consistently predicted PA, while teacher sensitivity had the strongest effect on PA change over time. These findings should be considered in the design of interventions aimed at supporting and enhancing school children’s PA and emotional development.
Acknowledgement
The authors would like to express their gratitude to the city of Vaasa, the main funder, as well as to the foundation Högskolestiftelsen i Österbotten, the Swedish Cultural Foundation, and the foundation Svensk-Österbottniska Samfundet for their funding of the Ostrobothnian CONSENSUS project. Additionally, we thank Åbo Akademi University for supporting the doctoral studies of the first author, Mölsä.
Declarations
Competing Interest
We have no known conflict of interest to disclose.
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Developmental Patterns of Positive Affect Among Preadolescents in Primary School: A Two-Year Latent Growth Analysis of Peer, Teacher, and Family Influences
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