Motivation-difficulty profiles in doctoral writing: a multivariate analysis of native and non-native speakers of English
Iman Ibrahim Khudhair, David N. BootePurpose
Writing support programs typically assume non-native English speakers require more intensive interventions than native speakers, yet no quantitative research has examined whether motivation and technical difficulties align with language background. The purpose of this study is to identify distinct motivation-difficulty profiles among doctoral students and examine how these profiles distribute across native and non-native English speakers.
Design/methodology/approach
Multivariate analysis examined writing difficulties and motivation among first-year doctoral students (n = 111) at a large research university. Factor analysis identified three writing difficulty dimensions and two motivation factors. K-means cluster analysis identified naturally occurring motivation-difficulty profiles.
Findings
Non-native speakers reported significantly greater difficulties across sentence mechanics, academic style and critical analysis (η2p = 0.099–0.128) but showed similar motivation levels to native speakers. Cluster analysis revealed three profiles that explained substantially more variance (η2p = 0.443–0.658) than language background alone: “Disengaged Writers” (35%), “Motivated Strivers” (26%) and “Confident Writers” (39%). Non-native speakers concentrated in high-difficulty profiles (88%) but distributed across both motivated and disengaged groups. Native speakers distributed across all three profiles, including 32% in “Disengaged Writers” despite moderate self-assessed abilities.
Research limitations/implications
Future studies should validate these profiles across institutional contexts and examine whether they remain stable or represent transitional states. Longitudinal research tracking students’ profile trajectories would clarify developmental patterns. Intervention studies testing profile-matched support against traditional approaches would establish whether this framework improves outcomes beyond current language-based models.
Practical implications
Current funding and staffing models organized around language background systematically misallocate resources. Profile-based assessment would improve resource allocation by identifying which students need technical support alone, which need motivation-focused interventions and which need both simultaneously. Implementation requires intake systems evaluating technical difficulties and motivational patterns together, plus staff training to distinguish high-difficulty/high-motivation students from high-difficulty/low-motivation students despite similar skill profiles.
Originality/value
Prior research examined writing difficulties and motivation separately or compared populations using different data sources, preventing standardized comparison. This study provides the first quantitative evidence that perceived competence and autonomous motivation operate independently in doctoral writing, challenging deficit models linking technical difficulties to disengagement. Naturally occurring profiles explained four to six times more variance (η2p = 0.443–0.658) than language background (η2p = 0.099–0.128), demonstrating empirically what qualitative research suggested but could not quantify: current support structures organized around native/non-native categorization systematically mismatch students’ actual needs. This person-centered approach reveals patterns that variable-centered methods obscure and provides empirical basis for reconceptualizing doctoral writing support.