**4. Limitations**

Yet, developing nations without the human or financial capital to gather data and conglomerate datasets will remain behind developed nations. As a result, developing nations need to prioritize widespread survey administration to build local and national datasets to be able to engage with larger, internationalized datasets, thus joining the global data community to use data to make informed decisions regarding education policy and practice. First, however, governments need to inventory their current data collection procedures and consolidate efforts to begin working toward robust, longitudinal datasets. Then, as developing nations are generating the capacity to perform this survey work, researchers and policymakers in these countries could begin to learn how other countries use technologies such as SDMX to explore how their own country could utilize and benefit from such a resource. Finally, educational leaders need to engage with these data to make equitable decisions and allocate resources to the most marginalized communities, rather than merely collecting and reporting on the data.

#### **5. Equity Issues Related to Survey Instruments and Data Collection**

As mentioned above, there are five primary hurdles to developing survey instruments and mass-collecting data in developing countries:


Once developing nations have negotiated these five hurdles, it becomes crucial that initial or current survey instruments and data collection strategies are built with equity in mind. Robust datasets can be powerful tools for educational stakeholders; however, depending on how robust the data collection is and what variables were included within data collection instruments can either strengthen or weaken its utility. One way for datasets to become more robust is to expand the survey instrument to gather specific demographic characteristics beyond what is typically gathered. Most survey instruments meant to capture educational data include demographic questions about a respondent's race and/or ethnicity, gender identity and/or sexual orientation, religion, and other salient identities. However, many survey instruments deployed by the most developed countries, such as the United States, do not adequately specify groups of people, especially given the long history of immigration to the United States from countries around the world. By including more specific questions about the participant's identities, the dataset allows users to explore potential trends within and between groups. Whether the differences are stark or nuanced, the ability for users to compare and contrast data between and within groups allows for better data analysis.

The following sections will detail three examples of why it is important to expand the questions about participants' identities. While not exhaustive of all identities, we highlight examples of how gathering specific, accurate information on participants' identities is critical to advancing equity in education through large datasets.
