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. 2025 Sep 24;95(4):1360–1368. doi: 10.1111/bjep.70035

Peer relationships and student motivation: Discussion of methodological approaches

Marion Reindl 1,✉
PMCID: PMC12590918  PMID: 40991308

Abstract

Background

As theoretical perspectives on peer relationships and motivation expand, new methodological challenges emerge in capturing the complex, dynamic and context‐sensitive nature of these associations.

Aims

This discussion paper reviews nine studies from this special issue to identify methodological innovations and limitations in current research on peer relationships and academic motivation.

Samples and Methods

A broad range of methodological approaches is compared, including research design (quantitative vs. qualitative), operationalization of constructs, levels of analysis, timing of study and strategies for modelling change.

Results

Several methodological challenges are identified, including defining the target population, assessing motivational constructs at both trait and state levels and distinguishing between perceived and actual peer data. Additional concerns include representativeness, measurement specificity and the need for analytical models that capture intraindividual developmental trajectories at different levels of analysis.

Conclusion

The paper highlights diverse methodological approaches that offer deeper insights into the dynamic relationships between peer relationships and academic motivation and may inform future studies on possible methodological approaches.

Keywords: academic motivation, methodological approaches, peers

INTRODUCTION

The special issue, Peer Relationships and Student Motivation, reflects growing research interest in the associations between peer dynamics and academic motivation. Alongside theoretical advancements, researchers increasingly encounter conceptual and methodological challenges, as noted in the Editorial (Daumiller & Hemi, 2025). Beyond the familiar difficulty of disentangling selection and socialization processes—often addressed through social network analysis—new methodological challenges have emerged in this context, such as the inclusion of state‐ and trait‐level aspects of motivation.

The studies reviewed in this issue address different aspects of peer relationships and motivation, employing a wide range of methodological approaches. They contribute valuable insights into the selection of research designs (e.g. mixed method designs), data collection methods (e.g. experience sampling methods) and analytical techniques (e.g. multilevel models) suited to specific research questions on peers and academic motivation. The following sections summarize the most important findings of the studies and discuss methodological implications.

STUDIES AND MAJOR FINDINGS

This section summarizes the methodological approaches and principal findings of each study, organized by their focus on motivation. It begins with studies addressing peer effects on more enduring, trait‐like aspects of motivation and continues with those examining more situational, state‐like forms. Within each category, studies are further arranged by the age group of the participants.

Kilday and Ryan (2025) conducted a cross‐lagged study of fifth and sixth graders in 46 classrooms, measuring behavioural, emotional and cognitive engagement in math and science. Using multilevel modelling, they found that while reciprocated friendships were unrelated to engagement, both higher friendship quality and greater network centrality were positively associated with all dimensions of engagement.

Schlesier et al. (2025) followed 1343 fifth‐grade students across three time points within a year, examining perceived relationship quality and intrinsic motivation. Latent profile and latent transition analyses identified four relationship profiles, with higher intrinsic motivation predicting shifts towards more positive relationship quality profiles.

Chen et al. (2025) applied a cross‐lagged design to data from 565,732 fifteen‐year‐olds in 20,227 schools across 75 countries to assess mastery approach goals. Multilevel structural equation modelling revealed that both peer cooperation and peer competition at the school level were positively associated with mastery goals.

Liem and Fredricks (2025) analysed data from 7324 students in Grades 5–12 using a national longitudinal sample. A random‐intercept cross‐lagged panel model indicated strong reciprocal effects between social and academic intrinsic values, particularly during early and late adolescence.

Mendoza et al. (2025) studied 766 students from 30 classrooms (Grades 7–10) to examine the achievement composition effect (ACE) and its interaction with intrinsic motivation and engagement. Linear mixed‐effects models showed that students' relative prior achievement predicted later achievement, with stronger ACEs among more motivated and engaged students.

Gaspard et al. (2025) studied two cohorts of university students (N = 320 and N = 498) over a semester, assessing academic expectancies, task values and perceived peer and faculty support. Cross‐lagged panel models revealed reciprocal links between confidence in receiving support and academic motivation.

Knickenberg and Zurbriggen (2025) used experience sampling over 1 week with fifth graders to assess situational motivation and peer interactions. Multilevel structural equation modelling showed that motivation was higher during peer interactions, partly explained by classroom climate.

Järvenoja et al. (2025) employed a mixed‐methods approach to study motivation regulation during collaborative learning among secondary students (ages 13–16). Using video recordings, self‐reports and recall interviews, they found that motivation regulation varied within lessons, across students and among learning groups, with peers primarily influencing each other through indirect strategies such as equal participation.

Seo et al. (2025) combined six focus groups (N = 15) and an experiment (N = 609) to explore how peer behaviours signal mindset beliefs in university students. They identified seven peer behaviours that shaped perceptions of peer mindset and influenced expected classroom experiences, including belonging, imposter feelings and academic risk‐taking.

METHODOLOGICAL CONSIDERATIONS

Quantitative versus qualitative research

Theoretical models explaining peer effects on academic motivation remain under development, particularly in accounting for the specific peer structures and processes that influence student motivation. This raises important questions for future research: Should studies adopt hypothesis‐driven approaches grounded in existing frameworks, or pursue exploratory approaches that extend these frameworks? Researchers must also decide whether to employ quantitative designs that enable broader generalization, qualitative designs that offer in‐depth insight into peer processes, or mixed‐methods designs that integrate both approaches.

The studies in this issue contribute to this discussion, with an emphasis on quantitative approaches. Quantitative studies require careful definition of the target population. In peer research, this definition may occur at different levels—the individual, the dyad or the peer group. Most of the reviewed studies sampled at the individual level, such as 15‐year‐old students (Chen et al., 2025), students in Grades 5–12 (Liem & Fredricks, 2025), fifth‐ and sixth‐grade students (Schlesier et al., 2025; Kilday & Ryan, 2025) and university students (Seo et al., 2025; Gaspard et al., 2025). These studies typically report demographic and educational background data that allow readers to judge representativeness.

The next step is to evaluate how closely each sample mirrors the characteristics of its intended target population. When individuals serve as the sampling unit, this process is relatively straightforward, as data on variables such as age, socioeconomic background and educational status are often available through national statistics. However, evaluating the representativeness of dyads (e.g. reciprocated friendships) or peer groups beyond the classroom (e.g. cliques) is much more difficult. Even when the target population is clearly defined, the question arises as to whether sufficient statistical data are available to evaluate the representativeness of characteristics at the dyad or group level?

Defining the target population in relation to a country's school system is another critical consideration, since structural features—such as class composition or transitions between school levels—shape peer interactions. This contextual information can inform sampling decisions, such as stratified sampling. Several reviewed studies note the educational context, albeit briefly, including those with participants from South Korea (Liem & Fredricks, 2025), the Philippines (Mendoza et al., 2025) and Germany (Schlesier et al., 2025). Such information can be used to assess the extent to which the results can be generalized within a country or across countries that share similar educational structures (e.g. from Germany to the Philippines). The cross‐national study by Chen et al. (2025) provides an important first step in exploring how peer effects vary across diverse countries and their educational systems.

In qualitative and mixed‐methods research, as that conducted by Seo et al. (2025), more detailed reporting on sample characteristics—such as friendship ties or roles within peer groups—would help readers assess whether multiple perspectives on a target population are adequately represented.

Sample size

Beyond assessing how well a sample represents the target population, it is also important to consider sample size for both qualitative and quantitative studies. In the quantitative studies reviewed here, sample sizes generally appear sufficient to detect the expected effects. However, when the sample size is very large (e.g. Chen et al., 2025), reporting effect sizes is essential, as even small effects can easily become significant. For moderate to large samples (e.g. Gaspard et al., 2025), conducting an a priori power analysis ensures that the study is adequately powered to detect the anticipated effects.

In experience sampling studies, sample size adequacy must be evaluated considering both the number of participants (Level 2) and the number of assessments per participant (Level 1). In Knickenberg and Zurbriggen's (2025) study, for example, 3099 situations were nested within 145 students. A well‐founded justification for the choice of sample size at the respective levels appears to be central, especially in ESM studies. The aim is to have a sufficient sample size to answer the research question appropriately, while at the same time reducing the effort for the participants, which is already high due to the frequent measurement points. Simulation‐based power analysis can be a first step toward determining the appropriate number of participants and how many measurements per participant are necessary (Tuerlinckx et al., 2025).

Justifying sample sizes is equally important in qualitative research, particularly with respect to saturation criteria (see Hennink & Kaiser, 2022). Seo et al. (2025) conducted interviews with six focus groups. Providing empirical information about the point of saturation relative to the research objectives would strengthen the interpretation of their findings.

Assessment of motivation

Recent revisions to classical motivation theories have adopted a situated perspective, emphasizing that academic motivation is embedded within social and cultural contexts that shape and give meaning to individuals' actions (Nolen, 2020; Nolen et al., 2015). Motivation should therefore be understood within its context, varying over time—from momentary, state‐level fluctuations to more stable, trait‐like patterns—and across levels of specificity, from general school motivation to subject‐specific motivation (Moeller et al., 2022).

The reviewed studies reflect this perspective, assessing both trait‐ and state‐like aspects of motivation at varying levels of specificity. Examples include general school‐related motivation (Liem & Fredricks, 2025; Mendoza et al., 2025; Chen et al., 2025), motivation related to specific university courses (Gaspard et al., 2025) and subject‐specific motivation in math and science (Kilday & Ryan, 2025). By contrast, state‐like assessments focused on science motivation (Järvenoja et al., 2025), situational enjoyment and concentration during particular learning episodes (Knickenberg & Zurbriggen, 2025). The level of specificity in assessing motivation has direct implications for how peer relations are measured. Whether peer relations are assessed at the school level, classroom level or within specific situations, these levels should conceptually align with the specificity of the motivational construct being measured or analysed. For example, Chen et al. (2025) assessed general motivation in combination with peer interactions at the school level; Kilday and Ryan (2025) assessed subject‐specific motivation in combination with peer relations at the classroom level and Knickenberg and Zurbriggen (2025) assessed situational motivation in combination with peer interactions within specific situations. Such alignment supports valid interpretation of peer effects.

Each study targeted a specific developmental phase and assumed the stability of motivational aspects within that cohort. However, as motivation becomes more differentiated and domain‐specific over time (Eccles & Wigfield, 2020), challenges emerge when using the same measures across developmental stages—particularly when studying peer effects during transitions such as from childhood to adolescence. Testing for measurement invariance across time, as demonstrated by Liem and Fredricks (2025) in their study spanning Grades 5 to 12, can help ensure construct consistency (Putnick & Bornstein, 2016).

The reviewed studies assessing trait‐like aspects of motivation generally meet established psychometric standards. By contrast, psychometric foundations for assessing state‐like aspects are currently under development (Moeller et al., 2023). This gap is also evident in the reviewed studies. Järvenoja et al. (2025) assessed motivational aspects with single items, which may limit validity. Knickenberg and Zurbriggen (2025) avoided single‐item measures, yet reliability remained limited. In this context, Tuerlinckx et al. (2025) bring together promising ideas on how the reliability and validity of ESM measures can be ensured in future studies (e.g. through planned missing designs).

Assessment of peer relationships: Actual versus perceived peer data

The Person‐Process‐Context‐Time model (Bronfenbrenner & Morris, 2007) is often used to explain the associations between peer relationships and motivation (e.g. Skinner et al., 2022). This model explains individual development, such as motivation, through specific structural characteristics of the person and the environment, dynamic interaction processes and individuals' perceptions of these elements. The reviewed studies emphasize both the structural (e.g. social values and peer norms) and process‐oriented aspects of peer interactions (e.g. classroom competition).

Some studies focus on perceived peer structures and processes, such as perceived friendship quality (Kilday & Ryan, 2025; Schlesier et al., 2025) and perceived peer cooperation and competition (Chen et al., 2025). Perceptions offer valuable insight into how students interpret their environment. However, they may also introduce bias. For example, peer cooperation might be perceived positively by some students even when no actual in‐class interaction occurs.

Several studies move beyond self‐reports, using aggregated achievement data (Mendoza et al., 2025) or network centrality measures derived from peer nominations to capture classroom involvement (Kilday & Ryan, 2025). Such data collection requires complex research designs. When the classroom is the unit of analysis, the peer group is institutionally preassigned, making it relatively straightforward to link aggregated classroom‐level data to individual outcomes. However, when peer relations form voluntarily—such as friendships or cliques—researchers must first identify relational patterns, often via sociometric methods. Once identified (e.g. reciprocated friendships and clique membership), these peer characteristics can be linked to individual motivation measures and perceptions. This process is more straightforward within fixed structures like classrooms but becomes more complex in contexts such as universities or informal peer networks outside of school.

In addition, several studies examined activity‐specific peer processes (Eccles, 2007) that influence motivation, such as motivation regulation (Järvenoja et al., 2025) and behaviours that foster mindset development (Seo et al., 2025). These processes may be observable in student communication. For example, Järvenoja et al. (2025) used classroom video recordings to directly observe peer interactions, providing valid insights into how lessons can be structured to encourage student motivation. However, enhancing ecological validity remains challenging, as many relevant peer interactions occur in less observable spaces outside the classroom (Kindermann, 2016).

Some socialization processes can also take place unconsciously and therefore cannot be directly observed or verbalized. To address this, researchers may use video data in combination with post‐hoc (stimulated recall) interviews (Järvenoja et al., 2025), allowing students to reflect on recorded interactions and reveal underlying processes. Another promising—though not yet used in the reviewed studies—approach is smartphone‐based proximity tracking (via Bluetooth signals; e.g. van Woudenberg et al., 2020). This method could capture peer influences that students are not aware of and, if the device is carried continuously, provide insight into interactions beyond the classroom.

Level of analysis: Within‐person, between‐person and between‐groups

Building on the distinction between state‐ and trait‐like aspects of academic motivation (Wasserman & Wasserman, 2020) and findings on motivational variability across peer groups (Kilday & Ryan, 2022), research shows that motivation varies across multiple levels: within persons (state‐level), between persons (trait‐level) and between groups (e.g. differences between friendship groups, cliques or classrooms). Multilevel models are therefore well suited to partition variance systematically and estimate peer effects at these different levels.

Knickenberg and Zurbriggen (2025) applied a multilevel structural equation model to examine different forms of peer effects, such as peer interactions and classroom climate, on aspects of motivation at the within‐ and between‐person levels. Kilday and Ryan (2025), Chen et al. (2025) and Mendoza et al. (2025) focused on between‐person and between‐group effects, employing different modelling strategies. For example, Chen et al. (2025) used a doubly latent variable approach, which accounts for measurement error in independent variables. Järvenoja et al. (2025) investigated all three levels—within individuals, between individuals and between learning groups—using linear mixed‐effects models (LMMs) that accounted for the fixed effects of four instructional phases and the random effects of students and groups.

However, in the application of multilevel models, several conceptual and methodological challenges remain. For predictors, it is essential to clarify both the level at which peer effects are hypothesized to operate (state or trait) and how the variables are modelled (e.g. specified at a single level or aggregated across levels). For outcome variables, complexity increases when no group‐level variance in motivation is observed, as in Knickenberg and Zurbriggen (2025). This raises the question of the level at which these effects can be meaningfully analysed if the specificity of the measurements, such as peer relations at the class level, does not correspond to the level of analysis for the motivational aspects.

These issues become even more complex when examining dynamic relationships between state‐ and trait‐level motivation over time, including top‐down and bottom‐up processes. Addressing these questions requires models that integrate bidirectional influences across time and levels while simultaneously accounting for peer effects that contribute to motivational change. Multilevel survival analysis may be a suitable method for this purpose (Tuerlinckx et al., 2025).

Timing of study

Fogel (2011) suggests selecting time frames in which peer effects are initially weak but increase over time. This approach allows for a more detailed understanding of how such effects develop. However, if data are collected too early or too late, studies may fail to capture the full magnitude of peer influence. Studies based on a single measurement occasion (Kilday & Ryan, 2025; Chen et al., 2025) provide valuable insights into peer effects at specific developmental stages. These findings can be extended through longitudinal designs, which make it possible to examine when peer influences are most pronounced. For instance, the study by Liem and Fredricks (2025), which followed students from Grades 5 to 12, enabled year‐by‐year analyses of peer dynamics. When multiple age groups are included, age‐specific analyses can further reveal how peer effects differ across various stages of adolescence.

Beyond ontogenetic considerations, the timing of data collection should also account for characteristics of the educational setting. For example, Liem and Fredricks (2025) examined peer effects across grade levels and school transitions. Such structural conditions can shape students' peer environments and, in turn, influence their experience of social values, thereby acting as potential confounding factors that must be addressed when designing longitudinal studies. In addition to broader structural transitions, the timing of measurements within the school year also matters. Peer effects may vary depending on whether data are collected at the beginning, middle or end of the academic year, as peer relationships often need time to reorganize and develop (Kindermann, 2016). Schlesier et al. (2025) explored friendship quality at different points in the school year to assess its stability. Aligning data collection with key instructional periods (Järvenoja et al., 2025) or clearly reporting the timing within the school year (Kilday & Ryan, 2025) can be especially valuable for identifying when peer influences are most salient—whether during the academic year as a whole or within specific learning sessions.

Closely related to the timing of studies is the selection of time lags between measurement points. The reviewed studies illustrate a wide range of time lags, reflecting diverse research goals. Shorter intervals, such as the 8‐week lag by Mendoza et al. (2025), can capture more immediate shifts in motivation. Longer intervals, such as the 1‐year lag by Liem and Fredricks (2025), may provide insights into broader developmental patterns shaped by peer dynamics. However, very long intervals risk overlooking smaller, yet meaningful, shifts in peer‐related effects on motivation.

Using ESM to assess state‐like motivation and peer effects, Knickenberg and Zurbriggen (2025) employed randomized data collection with a minimum time lag of 45 minutes, guided largely by practical considerations. By contrast, Järvenoja et al. (2025) aligned data collection with specific instructional phases within a single lesson, reflecting a more theory‐driven approach to capturing context‐dependent changes in motivation. These examples illustrate some design options in ESM. At the same time, research is needed about the optimal number of measurement occasions and the most appropriate time lags for capturing the temporal sensitivity of different motivational constructs (Pekrun, 2023).

Analytical strategies for investigating change

Laursen and Veenstra (2021) propose that peer influences should be conceptualized as directional changes, raising the critical question of how change is understood theoretically and examined methodologically. Theoretically, change is often viewed as intraindividual, referring to fluctuations within an individual over time. Gaspard et al. (2025) investigated change using a cross‐lagged panel model (CLPM). While this approach can identify temporal associations between variables, it primarily captures changes in individuals' relative rank order and does not address intraindividual variability (Hamaker et al., 2015).

An alternative approach that addresses intraindividual changes is the random‐intercept CLPM, applied by Liem and Fredricks (2025). This method accounts for both initial differences between individuals and changes within individuals over time (Mulder & Hamaker, 2021). Schlesier et al. (2025) adopted a person‐centred perspective, examining changes in students' relationship quality profiles across the school year using latent transition analysis (LTA). This approach captures transitions between profile memberships over time, offering insights into how students shift among qualitatively different relationship patterns.

An important next step is to refine analytical strategies, particularly in analysing short‐term changes in state‐level measures. It is essential to examine both variance within students (i.e. fluctuations in their motivation) and variance in change between students (i.e. differences in motivational trajectories). For example, if motivation is measured four times during a class period, change models can capture these dynamics. However, if motivation is measured five times per day over the course of a week, the complexity increases. In such cases, specialized analytical methods—such as time series analysis (Haslbeck & Ryan, 2022)—may be necessary to identify whether motivation in a given subject (e.g. mathematics) increases, decreases or remains stable over time as a function of specific peer interactions.

Summary and future directions

Overall, the nine papers reviewed demonstrate the value of employing different methodological approaches, ranging from large‐scale, macro‐level to fine‐grained micro‐level designs. They also highlight the advantage of using both hypothesis‐driven and exploratory frameworks, investigating a broad range of age groups from early adolescence to university and employing various methods to assess peer‐specific structures, processes and motivational constructs (e.g. questionnaires and interviews) alongside multiple data collection techniques (surveys, experience sampling and classroom observations). Moreover, the studies applied robust analytical tools to examine intraindividual and interindividual variations in motivation, as well as to predict these variations through characteristics of the peer environment. Across both state‐level and trait‐level motivational aspects, the findings consistently underscore the critical role of peer environments in shaping motivational outcomes across age groups.

For future research, several methodological aspects appear to be central: (1) accounting for both state‐level and trait‐level aspects of motivation; (2) aligning longitudinal research designs with individual developmental trajectories and (3) considering both students' subjective perceptions and actual peer data.

Initially, student development can be investigated through variable‐centred approaches, as in the eight reviewed studies, which assume that peer effects operate similarly across all students. However, these can be complemented by one study that used a person‐centred approach, which identified subgroups of students for whom peer effects differ. Additionally, idiographic approaches—which treat each individual as a unique unit of analysis with their own developmental pathway—may provide deeper insight into personalized patterns of motivational change (Beck & Jackson, 2020). Integrating these perspectives, while taking into account the complexity of the associations between peer relationships and motivation, remains a significant task for future research.

AUTHOR CONTRIBUTIONS

Marion Reindl: Conceptualization; writing – original draft.

CONFLICT OF INTEREST STATEMENT

The author declare no conflict of interest.

ACKNOWLEDGEMENT

Open Access funding provided by Paris Lodron Universitat Salzburg/KEMÖ.

Reindl, M. (2025). Peer relationships and student motivation: Discussion of methodological approaches. British Journal of Educational Psychology, 95, 1360–1368. 10.1111/bjep.70035

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