What are the best practices for collecting data on language change?

1 Types of data

When researching language change, the type of data used will depend on the research question and the availability of sources. Historical data, such as manuscripts, books, letters, newspapers, speeches, and recordings, can provide direct evidence of language change; however, factors like genre, style, audience, editing, and preservation can affect them. Comparative data from different varieties or dialects of a language can reveal divergence or convergence over time and show how it is impacted by geography, contact, and identity. Experimental data from controlled or elicited tasks can test how speakers use or perceive certain linguistic features in various contexts and capture how language change is influenced by cognition, attitude, and motivation.

2 Data quality

When studying language change, it is essential to ensure that your data is valid and reliable. This means paying attention to aspects such as completeness, representativeness, and accuracy. Your data should cover enough samples of the language feature you are looking at, across different time periods, varieties, or situations. It should also reflect the actual usage or variation of the language feature and account for the diversity and variability of the language community you are studying. Additionally, data should be collected and transcribed with care and precision, following standards and conventions of the field. It is also important to verify and cross-check data sources and methods, as well as acknowledge any limitations or uncertainties that could affect your results.

3 Data analysis

To extract meaningful and relevant information from your data, you need to apply appropriate and rigorous methods of data analysis. This includes describing your data in detail using descriptive statistics, tables, charts, graphs, etc., as well as labelling the linguistic features you are examining with relevant categories, codes, symbols, etc. You should also explain your data in relation to your research question and hypothesis, providing evidence and arguments for your claims. Additionally, you should evaluate your data in relation to the existing literature and knowledge and discuss the implications of your findings. It is important to consider the applications of your findings, as well as the limitations and directions for future research.

4 Data ethics

In order to respect the rights and interests of the people and communities involved in your data collection, you must adhere to the principles and guidelines of data ethics. This includes obtaining informed and voluntary consent from your data sources or participants, protecting their privacy and identity, using the data responsibly, and acknowledging any collaborators or contributors. Anonymization, encryption, secure storage, and other measures should be implemented to ensure the confidentiality of the data. Additionally, you should observe any rules and regulations set forth by your institution or funding agency. Collecting data on language change is a complex yet rewarding task that can give you a better understanding of human language diversity and dynamism. By following these best practices, you can ensure that your data is of high quality, relevance, and integrity for your linguistic research.

5 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?