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You’ve collected data on language change. How can you analyze it effectively?

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Language change is a fascinating phenomenon that affects how people communicate and express themselves. As a linguist, you may have collected data on language change from various sources, such as corpora, surveys, interviews, or experiments. But how can you analyze this data effectively and draw meaningful conclusions? In this article, we will explore some tools and methods that can help you with your linguistic analysis of language change.

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Kristīna Korneliusa

Junior Lecturer & Research Assistant at Latvijas Universitate | PhD Student | Linguist | Translator | Content Creator

1 Define your research question

Before you dive into your data, you need to have a clear and specific research question that guides your analysis. What aspect of language change are you interested in? How does it relate to the linguistic theory or framework that you are using? How can you operationalize your variables and measure your outcomes? Having a well-defined research question will help you narrow down your focus, select the appropriate tools and methods, and avoid getting lost in the data.

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2 Choose your analysis tool

Depending on the type and amount of data that you have, you may need different tools to analyze it. For example, if you have quantitative data, such as frequencies, distributions, or correlations, you may want to use a statistical software, such as R, SPSS, or Excel, to perform calculations, tests, and visualizations. If you have qualitative data, such as texts, transcripts, or recordings, you may want to use a software for content analysis, such as NVivo, Atlas.ti, or AntConc, to code, annotate, and search your data. You may also want to use a software for linguistic annotation, such as Praat, ELAN, or CLAN, to mark and analyze features like phonetics, prosody, or syntax.

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Kristīna Korneliusa

Junior Lecturer & Research Assistant at Latvijas Universitate | PhD Student | Linguist | Translator | Content Creator
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The analysis tools I could add to the list include Sketch Engine and LancsBox. But be careful with semantic tagging - bear in mind that there are words that have changed their meaning over time.

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3 Clean and organize your data

Before you can analyze your data, you need to make sure that it is clean and organized. This means that you need to check your data for errors, inconsistencies, outliers, or missing values, and correct or remove them as needed. You also need to organize your data in a way that makes it easy to access and manipulate. For example, you may want to create a database or a spreadsheet with your data, and label your columns and rows with meaningful names and categories. You may also want to create a codebook or a glossary that explains the codes, symbols, or terms that you use in your data.

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Kristīna Korneliusa

Junior Lecturer & Research Assistant at Latvijas Universitate | PhD Student | Linguist | Translator | Content Creator
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Depending on your research question, be sure to categorise your data according to years/decades and/or genre, topic, author etc.

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4 Apply your analysis method

Once you have your data ready, you can apply the analysis method that suits your research question and your data type. For example, if you want to compare the frequency of a linguistic feature across different groups or time periods, you may want to use a chi-square test or a t-test to see if the difference is statistically significant. If you want to explore the patterns or trends of a linguistic feature over time, you may want to use a regression analysis or a time series analysis to see if there is a linear or nonlinear relationship. If you want to understand the meaning or function of a linguistic feature in context, you may want to use a discourse analysis or a pragmatics analysis to see how it interacts with other linguistic and non-linguistic elements.

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5 Interpret and report your results

The final step of your analysis is to interpret and report your results. This means that you need to explain what your results mean in relation to your research question, your linguistic theory or framework, and the existing literature. You also need to acknowledge the limitations, assumptions, or implications of your analysis, and suggest directions for future research. You should present your results in a clear and concise way, using tables, graphs, charts, or diagrams to illustrate your points. You should also cite your sources and follow the conventions of your discipline or journal.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You’ve collected data on language change. How can you analyze it effectively?

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1 Define your research question

2 Choose your analysis tool

3 Clean and organize your data

4 Apply your analysis method

5 Interpret and report your results

6 Here’s what else to consider

Linguistics

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