Introduction
Academics and English language learners in linguistic studies often use first-person pronouns to represent their own identities as writers. As a result, there is a growing interest in researching how English language learners use first-person pronouns in their everyday writing and academic essays. Additionally, using first-person pronouns in academic writing will become a crucial rhetorical strategy for establishing an authorial presence.
First-person pronoun usage in academic writing has drawn the attention of numerous researchers both domestically and internationally. They are dedicated to comparative research, primarily from different fields, demonstrating how disciplinary distinctions influence the use of first-person pronouns. The authors compared the first-person topic pronouns “I” and “we” in scholarly publications written in five different languages.
Linguistic Variables
A verb is a word that acts, such as throw, provide, or explain; nevertheless, action verbs can be used in sentences containing both direct and indirect objects. Direct and indirect variables are frequently used to expand the meaning of verbs. A direct object is the target of a verb’s action, which is used in response to the questions “who” or “what.” The verb “throw,” for instance, typically has a direct object that describes what was thrown.
Direct objects are affected immediately by the verb, which is the primary distinction between direct and indirect objects. Some academics from different nations have compared the use of first-person pronouns in works written in English by native speakers (NES) versus non-native speakers (NNS). NES and NNES researchers conducted a comparison of first-person pronoun usage in biology papers (Pearce, 2021). For instance, it focuses on the use of first-person pronouns in distinct parts and their discourse functions.
Discussions
Overview
The observed results indicate that “me” is more popular than “us” with 491 instances, compared to 391 instances of “us”. The variation is spread across the different variables as presented in Table 19 in Appendix G. The speakers decten2y10i005a and decten2y10i009a have the highest total use of the first-person pronouns with 49 instances each. decten2y07i001a and decten2y07i009a have the least usage of the pronouns, registering 31 cases each. The distribution of the results is unique with each social factor, making them more interesting to interpret and draw conclusions.
Variables
The analysis takes into account six variables: speaker, gender, age group, social class, occupation, and the highest level of education achieved. The p-values indicate the influence of the different variables on the use of first-person pronouns. All p-values are high, implying that the variables influence the use of the tokens except in the case of social class. Only two social classes are considered in the data collection and analysis: the working and the middle class. The small p-value (0.491766) indicates that the variable has a negligible effect on the use of the tokens “me” and “us” among the different speakers.
Occupation
Occupation seems to play the most significant role in the variations of “me: and “us” with a p-value of 0.999186, followed by the speaker with a p-value of 0.998046. The speakers are defined by various factors, such as age, gender, and social class, which distinguish them from one another, making the results more elaborate and discernible. While the values of the two variables are very close to one another, their statistical impact is far-reaching, especially when a large dataset is considered. The results of the analysis are in agreement with Beal’s description of variables. This is because each variable can be used to predict the use of the given tokens (“me” and “us”) as well as other tokens in written and spoken speech.
Gender and Education
Females dominate males in the use of all pronouns across different social phenomena. A review of the use of pronouns by speaker occupation, presented in Table 6 in Appendix B, reveals that university students are the most prominent users of first-person pronouns, leading in all six pronouns. College students with 128 instances closely follow them. Shop workers, recent law graduates, and the retired category are the least likely users of personal pronouns, with 36 cases each.
A look at the use of pronouns with respect to education level shows that those who reached the university level are the most prominent users of “me” and “us,” followed by the “further education” category. The working class outdoes the middle class in pronoun usage, registering 534 and 269 cases respectively. There is a more pronounced use of “me” and “us” among the speakers aged under 30 years than those aged 40 and above.
Role of Statistical Methods
Considering this, overall statistical approaches can be classified into two types: descriptive methods and inferential methods. Having a data point that allows for calculating the linear magnitude between the two variables is also helpful. The correlation is an angular value that reflects how closely the data align with a single direction. In these other terms, the correlation assesses the strength and direction of the linear connection between the two relevant variables.
A statistically significant link is substantial enough in magnitude to be unlikely to occur in the collection if no relationship exists in the population. In demonstrating cause-and-effect linkages based on experimental evidence, the question of whether a finding is unlikely to arise by chance is crucial (Pearce, 2021). If an experiment is carefully prepared, randomization ensures that the various treatment groups appear comparable at the start of the trial, aside from the chance of the draw, which determines who is assigned to a particular group. Suppose participants are treated consistently throughout the trial. Therefore, demonstrating that blind luck is a poor description of a sample connection gives vital proof that the therapy had an impact.
Statisticians define standardized predicted values as standardized effect sizes because they show the strength of the link between variables without relying on the original data units. Alternatively, this metric expresses the magnitude of influence in terms of standard deviation. Mean effect size helps to appreciate the practical significance of the findings. The linguistic region described as northern English is highly heterogeneous, to summarize, both in the linguistic background and synchronic variability trends. Some nuances emerged during the data collection process for the questionnaire used as the basis for WAVE.
Conclusion
In brief, each of the factors examined in the dataset has an impact on the use of the token. The variation in the distribution of the observed and expected results is in line with Beal’s description of the variable, which encompasses the probability of smoothing every variable to study communities. The p-values obtained can be used to predict or ascertain whether a given variable can be used to define the character of a given community. The results do not present an interesting pattern, as they reflect the real variations in life.
Reference
Pearce, M. (2021). The Participatory Vernacular Web and Regional Dialect Grammar: A New Account of Pronouns in North East England. English Today 37.4: 196-205. Print.
Appendices
Appendix A – Quantitative Analysis by Speaker Code
Table 1. Total observed results by speaker code
Table 2. Expected total results by speaker code
Table 3. (Observed-expected)^2/expected by speaker code
Appendix B – Quantitative Analysis by Gender
Table 4. Observed use of pronouns by gender
Table 5. Expected results by gender
Table 6. (Observed-expected)^2/expected by gender
Appendix C – Quantitative Analysis by Age Group
Table 7. Use of pronouns by age group
Table 8. Expected results by age group
Table 9. (Observed-expected)^2/expected by age group
Appendix D – Quantitative Analysis by Social Class
Table 10. Use of pronouns by social class
Table 11. Expected results by social class
Table 12 (Observed-expected)^2/expected by social class
Appendix E – Quantitative Analysis by Social Occupation
Table 13. Use of pronouns by occupation
Table 14. Expected results by occupation
Table 15. (Observed-expected)^2/expected by occupation
Appendix F – Quantitative Analysis by Education
Table 16. Observed results by education
Table 17. Expected results by education
Table 18. (Observed-expected)^2/expected by education
Appendix G – Summary of P-values
Table 19. p-values of different variables