The issue turns on a deep divide in measurement theory between what psychometricians call reflective and formative constructs. In a reflective model, an invisible underlying trait—here, a latent quantity of home resources—causes the observable indicators to vary, and the indicators’ mutual correlations determine how much each one counts. In a formative model, the arrows point the other way: the observed components together constitute the composite, and their weights should be justified by what they actually accomplish, such as predicting achievement. Meng and Bolt argue that HRL has always had the hallmarks of a formative index. Books, study supports, parental education, and parental occupation are not interchangeable echoes of a single hidden variable; they are distinct resources that can substitute for one another, and a family can be educationally rich through many different combinations of them.

The dominant practice, however, treats HRL as a latent variable. This tradition brings powerful machinery, including tests of measurement invariance that check whether the indicators relate to the latent construct identically across countries. When those tests fail, researchers typically relax the model through partial invariance, allowing country-specific parameters for the offending items. Wendt and colleagues showed in 2017 that such scaling choices can change estimates of how strongly HRL relates to achievement, and later work on post-conflict societies in the Western Balkans found that the books-at-home item behaved differently across countries, apparently because war and displacement had depleted household libraries independently of other family characteristics. The standard response is to model that anomaly as differential item functioning—a measurement bias to be corrected. Meng and Bolt, drawing on Welzel and Inglehart’s critique and on a 2012 report on socioeconomic status for the US National Assessment of Educational Progress, suggest a provocative alternative: the missing books may not be bias at all, but a genuine resource difference that the index ought to capture.

To test the stakes, the researchers analyzed the 2011 cycle of TIMSS mathematics and PIRLS reading data, covering 185,475 fourth-grade students across 37 countries. Their central tool was a structural equation model in which the five HRL indicators load onto a single latent variable that in turn predicts achievement. If the latent variable faithfully summarizes the achievement-relevant information in its indicators, then each indicator’s factor loading should be roughly proportional to its direct predictive effect on test scores. Any leftover association between an indicator and achievement—detected through modification indices and standardized expected parameter change values—signals a structural violation: the latent variable is over- or under-representing that indicator’s real influence.

The pooled results were striking. Parent-reported number of books in the home carried the strongest loading on the latent HRL variable, about 0.69 for both mathematics and reading, yet its residual effect on achievement was positive and substantial, with expected parameter changes of roughly 0.16 to 0.17. In plain terms, books reported by parents predict achievement considerably more strongly than their loading on the latent variable implies. Parental occupation showed the opposite pattern: despite a healthy loading, its direct relationship with achievement was weaker than the latent model suggested, with negative expected parameter changes around −0.10 to −0.13. The authors offer an intuitive reading. Books in the home reflect not just material resources but a family’s active emphasis on education, whereas parental occupation conveys potential support without saying much about how learning is actually prioritized under the roof.

With the mismatch established, Meng and Bolt compared four ways of building the composite: a latent variable score with weights pooled across countries, a latent variable score with country-specific weights, an achievement-tailored composite with pooled regression weights, and an achievement-tailored composite whose weights come from regressing achievement on the indicators within each country. In the tailored approaches, the regression coefficients themselves become the indicator weights, so the composite is defined by how well its ingredients predict the outcome. Across the full sample, the choice mattered surprisingly little: the various composites correlated at roughly 0.95 or higher, and the average gain in explained achievement variance from tailored weights was only about 0.01 in R-squared. For most countries, the philosophical dispute is nearly academic.

But not everywhere. In a handful of systems, the differences were consequential. In Azerbaijan, the country-specific latent variable and tailored composites correlated at only 0.83, and switching from the latent variable index to the tailored one increased the proportion of explained mathematics achievement variance by about 33 percent. For reading, Norway saw a 44 percent proportional increase in R-squared, from 0.09 to 0.13, when tailored weights replaced country-specific latent variable weights; Ireland gained 22 percent and Saudi Arabia 25 percent. The pattern in Norway and Ireland was the mirror image of good measurement: the latent variable approach, which derives weights purely from how indicators correlate with one another, downweighted the proximal indicators—books and study supports—that actually predict achievement, while upweighting the distal indicators of parental education and occupation. Saudi Arabia displayed its own idiosyncratic profile, with study supports over-weighted and parental education under-weighted by the latent model relative to their true predictive roles.

Perhaps the most counterintuitive finding concerns what happens when researchers do the very thing invariance methodology recommends. Allowing discrimination parameters to vary by country is supposed to improve measurement, yet for many countries it actually reduced the prediction of achievement compared with pooled latent variable weights. The explanation lies in where the weights come from: country-specific loadings are estimated from the internal consistency of the indicators within that country, and there is no guarantee—indeed, no reason to expect—that changes in internal consistency track changes in predictive relevance. A parameter adjustment that makes the measurement model fit better can simultaneously make the composite a worse summary of the achievement-relevant resources a family provides. From a formative perspective, the authors note, partial invariance modeling does not merely fail to fix the problem; it can actively mute meaningful between-country differences by absorbing them into measurement parameters.

The study does not declare a winner. Tailored weights, the authors caution, guarantee better prediction almost by construction, since they are optimized for that purpose—a practice the Cowan report famously warned against, arguing that the relationship between socioeconomic status and achievement should be a finding rather than an optimization exercise. Latent variable weights, by contrast, are defined independently of the outcome and avoid overfitting, but they rest on the assumption that a single hidden cause generates all five indicators, an assumption that recent causal work by VanderWeele shows is frequently violated when indicators have their own direct effects on outcomes. Under such violations, regressing achievement on a latent variable likely underestimates the cumulative influence of the indicators, sometimes substantially.

The broader lesson reaches well beyond the HRL index. International assessments routinely convert background questionnaires into latent constructs—attitudes, motivations, socioeconomic statuses—and then police their comparability with invariance machinery that presumes reflective measurement. Meng and Bolt’s analysis suggests that when a construct is better understood as a bundle of resources than as a hidden trait, that machinery can distort rather than refine cross-country comparisons. The choice between defining equivalence by how indicators relate to each other or by how they relate to an external criterion is not a technical footnote; it changes which countries appear to have strong home-resource effects, by how much, and in which direction. For a field whose rankings move education policy worldwide, the composition of a single index number turns out to be anything but a small detail.

Subject of Research: Methodological comparison of latent variable and achievement-tailored approaches to constructing the Home Resources for Learning index in TIMSS and PIRLS

Article Title: Latent variable or achievement-tailored composite?: The home resources for learning (HRL) index in TIMSS and PIRLS

Article References: Meng, L., & Bolt, D. (2026). Latent variable or achievement-tailored composite?: The home resources for learning (HRL) index in TIMSS and PIRLS. Large-scale Assessments in Education, 14(1), Article 52. https://doi.org/10.1186/s40536-026-00324-8

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00324-8

Keywords: Home Resources for Learning, TIMSS, PIRLS, latent variable models, measurement invariance, formative indicators, reflective indicators, socioeconomic status, structural equation modeling, psychometrics, international large-scale assessments, educational achievement

The research, published in the journal Discover Education, drew on data from 918 students across 12 randomly selected community schools in Pokhara Metropolitan City. The team, led by Sudesh Pandit with colleagues including Ramesh Adhikari, Om Chandra Thasineku and Sushil Sharma, chose grade VIII deliberately: in Nepal’s school system, this is the year students sit a standardized examination administered by the Metropolitan Department of Education, marking their transition from basic to secondary education. Educational achievement was measured with a self-administered test built from multiple-choice items drawn from the National Assessment of Student Achievement and model questions from the Curriculum Development Centre. The test covered four core subjects, English, Mathematics, Science and Social Studies, with 20 items per subject. Items were reviewed by subject experts and pre-tested on 48 students from schools outside the final sample to check clarity, difficulty and content validity before the main study began.

The headline numbers are striking. Roughly three quarters of the students, 75.1 percent, were active commuters, meaning they walked or cycled to school, while the remaining 24.9 percent used motorized transport such as school buses, public vehicles or private cars. Passive commuters averaged 13.54 points on the composite achievement score, with a standard deviation of 3.19, while active commuters averaged 11.49 points, with a standard deviation of 2.62. That difference of just over two points on a 20-point scale represents a meaningful academic gap. By contrast, differences tied to commuting time and distance were small. Students who traveled more than 20 minutes scored only marginally higher than those with shorter trips, and distance from home to school showed essentially no relationship with achievement. What mattered, the data suggested, was not how far or how long students traveled, but how they traveled.

To test whether that pattern would survive statistical scrutiny, the researchers built a series of multiple linear regression models, adding blocks of control variables step by step. The first model adjusted only for commuting time and distance; the second added demographic characteristics such as age, sex and ethnicity; the third introduced socio-economic factors including parental education, parental occupation, household income and housing ownership; and the final model incorporated measures of the supportive environment, such as parental support at home, parental engagement in school activities and teacher support, along with indicators of student proactiveness like homework regularity, extracurricular participation, absenteeism and peer relationships. Active commuting remained significantly and negatively associated with educational achievement in every model. The standardized coefficient started at minus 0.319 in the first model, fell to minus 0.268 after demographic controls, minus 0.244 after socio-economic controls, and settled at minus 0.121 in the fully adjusted model, still statistically significant at p less than 0.001.

That shrinking but stubborn coefficient tells a nuanced story. Each layer of controls absorbed part of the raw gap, confirming that active commuters in Pokhara tend to come from families with fewer educational and economic resources. Maternal education, in particular, emerged as a strong socio-economic predictor of achievement, consistent with a large international literature linking mothers’ schooling to children’s academic outcomes. Yet even after fifteen control variables were accounted for, commuting mode retained an independent association with test scores. The authors checked the assumptions of their regression models carefully, examining residual plots for normality and homoscedasticity, confirming low autocorrelation with a Durbin-Watson statistic of 1.642, and verifying that multicollinearity was not a problem using variance inflation factors. Some variables, including father’s education and certain occupation categories, had to be dropped from the final model because of perfect multicollinearity.

Why would walking to school, so often promoted as a cognitive booster, be linked to poorer grades in this setting? The authors point to the starkly different meaning of active commuting in Nepal compared with high-income countries. In cities like Pokhara, walking or cycling to school is rarely a lifestyle choice; it is frequently an involuntary consequence of financial constraints, with narrow lanes, unsafe roads and difficult terrain making the daily journey a burden rather than a pleasure. Long, physically demanding trips can produce fatigue that erodes the energy available for homework and classroom concentration. Carrying heavy school bags over long distances adds a further psychological and physical toll, and prior research cited by the team suggests students forced into such commutes may feel less motivated toward their studies. Passive commuters, by contrast, arrive feeling safe, comfortable and rested, and more than 81 percent of them commute for more than 20 minutes each day, a duration that in the fully adjusted model showed a weakly positive association with achievement for this group alone.

The study’s demographic findings add further texture. The sample was 55.9 percent female and 44.1 percent male, with the largest ethnic group being Janajati at 38 percent, followed by Brahmin or Chhetri at 31.8 percent and Dalit at 27.6 percent. Sex showed no significant association with achievement, echoing international evidence that the gender gap in education is closing. Age and ethnicity did matter, with older students and certain ethnic groups scoring lower, but neither explained away the commuting effect. Household economics painted a familiar picture of urban Nepal: more than half of families earned between 21,000 and 40,000 Nepalese rupees per month, over half lived in rental housing, and a large share of fathers worked in foreign employment or daily wage labor while most mothers reported household work as their primary occupation. Parental support at home was reported by 59.7 percent of students, teacher support by 61.5 percent, and 78.6 percent participated in extracurricular activities, though the latter showed no significant link to achievement.

The findings carry real weight for policy. Nepal’s government has pursued a policy of ensuring schools lie within a 20-minute walking distance of children’s homes, and recent measures such as customs duty exemptions for school buses have expanded motorized options. Yet the School Sector Development Plan and the constitutional guarantee of free and compulsory basic education, the authors note, pay little attention to the environmental and geographical barriers that shape how children actually get to school. The study does not argue that walking is inherently harmful; rather, it argues that when active commuting is unsafe, exhausting and imposed by poverty, its cognitive benefits are unlikely to outweigh its structural and psychosocial burdens. The authors call for safe, comfortable, convenient and efficient commuting arrangements, suggesting that well-designed transport systems and safer routes could transform the daily journey from a drain on learning into a neutral or even positive experience.

As with any cross-sectional study, caution is warranted. The design captures associations at a single point in time and cannot establish that commuting mode causes differences in achievement; unmeasured factors such as neighborhood safety or school quality could still play a role. The authors themselves emphasize that the results should be interpreted as associations, not causal effects, and they call for longitudinal and mixed-method research to unpack why active commuting appears to work against academic success in this context despite its well-documented health benefits. Still, the study is a vivid reminder that the same behavior can mean entirely different things in different places. In Copenhagen or Portland, a child walking to school is exercising a healthy choice. In Pokhara, that same walk may signal a family’s lack of options, and the weight of that daily journey, the researchers suggest, follows the child into the classroom.

Subject of Research: The association between active and passive commuting modes to school and educational achievement among students in Pokhara, Nepal

Article Title: Association between commuting modes to school and educational achievement among students in Pokhara, Nepal

Article References: Pandit, S., Adhikari, R., Thasineku, O. C., & Sharma, S. (2026). Association between commuting modes to school and educational achievement among students in Pokhara, Nepal. Discover Education, 5(1), Article 1108. https://doi.org/10.1007/s44217-026-02252-9

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02252-9

Keywords: active commuting, educational achievement, Nepal, Pokhara, school transport, students, public health, socio-economic status, regression analysis, education policy, commuting time, Discover Education