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The Competence Model of Writing Skills in the project “Multilingual Development: A Longitudinal Perspective (MEZ)”

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Language Development in Diverse Settings

Part of the book series: Edition ZfE ((EZFE,volume 11))

Abstract

This article presents an approach for measuring the educationally relevant writing ability of secondary school students, as applied in the project Multilingual Development: A Longitudinal Perspective (MEZ). Due to the longitudinal design of the study, the aspect of competence measurement across time is essential. The participants were adolescent students with German-Russian, German-Turkish, and monolingual German language backgrounds. In four waves, they were tested in German (majority language), Russian or Turkish (languages of origin) as well as English, French and/or Russian (foreign languages at school). We present a cross-lingual, comprehensive competence model of writing skills and its operationalization through empirical indicators. Subsequent statistical modelling using a longitudinal latent measurement model allows the explicit testing of measurement characteristics such as measurement invariance. The results reveal that the postulated theoretical construct has been measured in all languages in a structurally comparable way, and the development of inter-individual differences over time can be reliably interpreted. However, the interpretation of intra-individual developmental patterns over time is not yet possible.

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Notes

  1. 1.

    In the German education system, the first foreign language is obligatory in primary school – the vast majority of children learn English. For roughly half of the population, namely, all those who attend academic track schools (e.g., “Gymnasium”, the classical German grammar school), a second foreign language is obligatory. Approximately 20 foreign languages are on offer in secondary schools, French and Russian among them. French is one of the most frequently chosen second foreign languages.

  2. 2.

    We also recruited and tested students in Russian as a foreign language, but despite considerable efforts, we did not reach a sample size sufficient to include their data in our statistical analyses. The data are nevertheless relevant for qualitative linguistic analyses; see, e.g., Krause and Dittmers (2019).

  3. 3.

    The German and English titles of the prompts were transferred to the languages in which they were presented to the participants.

  4. 4.

    As explained above, we also used these prompts for the testing of Russian as a foreign language but did not include this sample here, as the number of participants was not sufficient for our statistical analyses.

  5. 5.

    The total sample includes all cases for which information is available. The MPlus software package allowed us to use all the available information for the model estimation reported below by using the full information maximum likelihood (FIML) approach. This method involves calculating a case-related likelihood function based on the information observed for that case. Although there is no imputation of missing data, the available information for the incomplete cases is included in the parameter estimates. This procedure was used in all models reported here.

  6. 6.

    The increased drop-out of participants in wave 4 is mainly due to students in the older cohort who had finished school or changed schools and is therefore random (Heimler 2019, p. 22).

  7. 7.

    POMP scores (percentage of maximum possible): The students’ raw scores were standardized as the percentage of the maximum achievable [(raw score/maximum achievable score) * 100]. Since only task accomplishment has a natural maximum (27 points), the highest empirical score measured at all waves was used to set the maximum.

  8. 8.

    For the first wave, a positive residual correlation of task accomplishment and text length was allowed. The reason for this correlation is that – independent of the students’ writing skills – the first-wave coders systematically rated longer texts higher in terms of task accomplishment than shorter texts.

  9. 9.

    For the second wave, a positive residual correlation of task accomplishment and verbs was allowed. The reason for this correlation is again that – independent of the students’ writing skills – the second-wave coders for the Russian texts systematically rated longer texts higher in terms of task accomplishment than shorter texts.

  10. 10.

    The negative residual covariance at wave 2 indicates that the single-factor model overestimates the relationship between task accomplishment and verbs. The model thus tends to break down into more than one factor (Schermelleh-Engel 2015). This validation of the theoretical competence model for writing skills applied in MEZ is due to inconsistencies in the coding quality of the coders for the Turkish texts in wave 2. Therefore, the single-factor structure was stabilized by allowing the negative residual covariance.

  11. 11.

    The positive residual correlation between task accomplishment and text length at wave 1 accounts for a coder effect: independent of the students’ writing skills, the first-wave coders systematically rated longer texts higher in terms of task accomplishment than shorter texts.

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Authors and Affiliations

  1. Faculty of Education, Universität Hamburg, Hamburg, Germany

    Ingrid Gogolin, Thorsten Klinger, Birger Schnoor & Irina Usanova

Authors
  1. Ingrid Gogolin
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  2. Thorsten Klinger
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  3. Birger Schnoor
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  4. Irina Usanova
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Correspondence to Ingrid Gogolin .

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Editors and Affiliations

  1. Universität Hamburg, Hamburg, Deutschland

    Hanne Brandt

  2. Universität Hamburg, Hamburg, Deutschland

    Marion Krause

  3. Universität Hamburg, Hamburg, Deutschland

    Irina Usanova

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Gogolin, I., Klinger, T., Schnoor, B., Usanova, I. (2022). The Competence Model of Writing Skills in the project “Multilingual Development: A Longitudinal Perspective (MEZ)”. In: Brandt, H., Krause, M., Usanova, I. (eds) Language Development in Diverse Settings. Edition ZfE, vol 11. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-35650-7_2

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  • DOI: https://doi.org/10.1007/978-3-658-35650-7_2

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