How can data analysis strengthen your linguistic research paper arguments?

1 Choose the right data

The first step in data analysis is to choose the right data for your research question. Depending on your topic, you may need to use primary or secondary data, qualitative or quantitative data, or a combination of both. Primary data are data that you collect yourself, such as surveys, interviews, or experiments. Secondary data are data that you use from existing sources, such as databases, corpora, or literature. Qualitative data are data that are not numerical, such as texts, images, or audio. Quantitative data are data that are numerical, such as frequencies, scores, or measurements. You should choose the data that best suit your research question, and that are reliable, valid, and ethical.

2 Apply the appropriate methods

The second step in data analysis is to apply the appropriate methods for your data. Depending on your data type and research question, you may need to use descriptive or inferential statistics, content analysis, discourse analysis, or other methods. Descriptive statistics are methods that summarize and display your data, such as mean, standard deviation, or graphs. Inferential statistics are methods that test your hypotheses and draw conclusions from your data, such as t-test, ANOVA, or regression. Content analysis is a method that categorizes and counts the themes or features of your qualitative data, such as keywords, topics, or sentiments. Discourse analysis is a method that examines the structure and meaning of your qualitative data, such as grammar, pragmatics, or rhetoric. You should apply the methods that best answer your research question, and that are appropriate, accurate, and transparent.

3 Present the results

The third step in data analysis is to present the results of your methods. Depending on your data type and method, you may need to use tables, charts, figures, or text to present your results. Tables are useful for showing numerical data in a clear and organized way, such as frequencies, percentages, or statistics. Charts are useful for showing numerical data in a visual and comparative way, such as bar charts, pie charts, or line charts. Figures are useful for showing qualitative data in a visual and illustrative way, such as images, diagrams, or maps. Text is useful for explaining and interpreting your results in a concise and coherent way, such as describing the main findings, highlighting the significant differences, or relating the results to your hypotheses. You should present the results that are relevant, clear, and consistent.

4 Discuss the implications

The fourth step in data analysis is to discuss the implications of your results. Depending on your research question and results, you may need to discuss the limitations, implications, and recommendations of your data analysis. Limitations are the weaknesses or constraints of your data analysis, such as sampling errors, measurement errors, or methodological flaws. Implications are the meanings or consequences of your data analysis, such as theoretical contributions, practical applications, or social impacts. Recommendations are the suggestions or directions for future research or action, such as new questions, new methods, or new policies. You should discuss the implications that are relevant, critical, and constructive.