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Frequency and Variability in Errors in the Use of English Prepositions

Paper · 1996 · Rosa María Jiménez Catalán

Summary

This article examines why English prepositions are so hard for Spanish secondary-school learners. It asks three questions: which errors are most frequent in a descriptive composition, whether error rates differ across schools, and whether preposition errors cluster in particular linguistic contexts. The author gathered 290 compositions from three state secondary schools. Students wrote for thirty minutes about a photograph. The errors were coded with a purely formal taxonomy (omission, addition, substitution) and processed in DBASE III Plus. The study expects three things: preposition errors will rank among the top three of ten error types, their frequency will be similar across schools, and they will be found in certain linguistic contexts while being absent from others. The supplied extract omits the results section, so the actual figures and conclusions cannot be reported here. The work matters because earlier error analysis had mostly looked at phonology and morphosyntax. The few studies on prepositions tended to cover a handful of items or few informants, or gave no percentages. This study also extends research on context-driven interlanguage variability beyond phonological features.

Key points

  • The corpus has 290 essays by third-year EFL students from state schools in Madrid, Pamplona and Calahorra: 118 males and 172 females, aged 16–19, ranging from elementary to upper intermediate level.
  • The elicitation task was a 30-minute free description of a photograph taken from Byrne (1967). Students were told the essays would not be graded and that communication mattered more than accuracy.
  • An error is any departure from a norm of mature performance (Dulay, Burt and Krashen 1982). Standard English as described by Quirk and Greenbaum (1985) serves as the reference. Substitution is defined broadly and also covers spelling and misformation errors.
  • The author argues that learners cannot really avoid prepositions, because they are so numerous and do so much relational work. A high preposition error count is therefore read as genuine difficulty rather than an artefact of avoidance.
  • Contrastive predictions from the literature:
    • Spanish and Catalan speakers struggle with stranded prepositions ("who they're working for").
    • They carry over the preposition + infinitive pattern where English needs a gerund.
    • They overuse "in" where "on" or "at" is required (González 1992).

Interesting ideas

  • I1. Errors are classified only by their surface shape (a missing, extra or wrong item), without guessing at causes. This makes annotation consistent and keeps diagnosis as a separate later step.
  • I2. The elicitation task is chosen deliberately so that it creates many obligatory contexts for the target category. Describing a picture forces spatial relations, so error counts can be compared against known opportunities.
  • I3. Zero errors do not prove competence, because a learner may simply avoid hard constructions. A category is only a fair difficulty probe if it cannot be dodged.
  • I4. Error rates are expected to depend on linguistic context, for example the second clause of a compound sentence versus an independent clause. Errors should therefore be stratified by context rather than counted globally.
  • I5. Contrastive L1–L2 analysis predicts specific trouble spots in advance, such as preposition stranding and preposition + infinitive versus gerund.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Repair rules induced from witness-parser disagreements could be bucketed as preposition omission, addition or substitution before any causal explanation is attempted, which keeps the rule inventory auditable.
  • I2 (answer): Test sets for spatial and relational QA could be built from scene-description texts where prepositions are unavoidable, so accuracy can be measured against a known denominator of required relations.
  • I3 (back): Generation evaluation should check whether the system avoids stranded prepositions or complex PP attachments, not just whether its output is error-free.
  • I4 (tree): Parser preposition-attachment errors could be broken down by clause position and construction type, so the contexts where the perceptron parser or gparse fail are visible instead of hidden in one LAS number.
  • I5 (tree): These predictions are ready-made targeted test cases ("who they're working for", "after seeing the film"), useful for checking that stranded-preposition and gerund-complement analyses survive treebank dialect conversion.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksDevelopmental and Educational Psychology · 499 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,263 works

Cite

Cite as Rosa María Jiménez Catalán. 1996. Frequency and Variability in Errors in the Use of English Prepositions. Miscelánea: A Journal of English and American Studies 17.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.26754/ojs_misc/mj.199611043

@article{catalan1996frequency,
  title = {{Frequency and Variability in Errors in the Use of English Prepositions}},
  author = {Rosa María Jiménez Catalán},
  journal = {Miscelánea: A Journal of English and American Studies 17},
  year = {1996},
  doi = {10.26754/ojs\_misc/mj.199611043},
  url = {https://doi.org/10.26754/ojs_misc/mj.199611043}
}

References

36 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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