Microsoft Word - 49-4-Bauder.docx 1/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 The Creative Commons-Attribution-Noncommercial License 4.0 International applies to all works published by IASSIST Quarterly. Authors will retain copyright of the work and full publishing rights. Conceptions of data literacy in the statistics education literature Julia Bauder1 and Libby Cave2 Abstract Data literacy is an increasingly important skill in our data-driven world, and librarians and other information professionals can play a key role in creating a data literate population due to data literacy’s close association with information literacy. However, the definition of data literacy and the attention paid to certain competencies varies greatly between fields: what librarians and statisticians mean by “data literacy” is not the same thing. A scoping review of data literacy articles within the field of statistics education reveals the landscape of data literacy education in statistics, giving librarians and other information professionals a map for coordinating their data literacy work with disciplinary faculty. The areas of data discovery, evaluating and ensuring the quality of data and its sources, and reproducibility are closely examined. These areas are defined and valued inconsistently amongst information professionals and statisticians, but their close associations to traditional library services create an ideal opportunity for libraries and data archives to contribute to data literacy education. Keywords Data literacy, statistics education, reproducibility, data discovery Introduction Statistics educators often serve as the primary providers of data literacy education, but there is a disconnect between what statistics educators value in data literacy instruction and what librarians and other information professionals see as foundational data literacy competencies. By reviewing the data literacy literature from journals that publish articles in statistics education, we have found which areas of data literacy are less prioritized in statistics education. These gaps around developing students’ ability to find, critically evaluate, document, and preserve data align closely with the values and duties of librarians, data archivists, and research data management specialists, providing libraries with an opportunity to make substantial contributions to data literacy education. Literature review Data literacy is still an evolving field, and the exact definition of data literacy has not yet been settled. For example, Pinto et al. (2023) found in their systematic review of the literature that discussed both data literacy and information literacy that 45.59% of the 68 included articles provided their own unique definition of data literacy rather than citing a pre-existing definition. 2/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 Despite (or perhaps because of) this diversity of definitions, there have been several attempts to establish a consensus definition of data literacy. These efforts have typically involved comparing the competencies that are mentioned in competing definitions of data literacy in search of common themes or areas of overlap. For example, Bonikowska, Sanmartin and Frenette (2019) compared five different data literacy frameworks that were intended for use with broad, general populations of students or working adults. They found twenty-seven different competencies that were mentioned in just those five frameworks. Even worse, only five of the competencies appeared in all five frameworks: data discovery, data manipulation, evaluating and ensuring the quality of data and sources, basic data analysis, and data interpretation. Extending this line of work, Downes (2023) examined twenty different publications that provided lists of competencies that a person needed to achieve to be considered “data literate.” The origins of these twenty publications varied. Several were produced by government agencies, such as the Australian Bureau of Statistics and Statistics Canada; others were written by academic researchers. In those twenty works, Downes identified forty different data literacy competencies that were mentioned at least once, and not a single one of those forty competencies appeared in all twenty of the works that he examined. He did, however, find that these competencies tended to cluster into five different data literacy models: the data stewardship model, the analysis and decision-making model, the information literacy model, the science and research data literacy model, and the social engagement model (Downes, 2023, p. 109). Downes’ (2023) information literacy model for data literacy is an obvious bridge to the library’s domain, and librarians and other information professionals can find strong links from their skillset to the other models. Like information literacy, data literacy definitions are conceptualized as both a “specific skill set and a knowledge base, which empowers individuals to transform data into information and into actionable knowledge” (Koltay, 2017, p.17). While definitions of data literacy fluctuate in library and information science literature, the most cited data literacy competencies are “access, interpret, critically evaluate, manage, handle, and ethically use data” (Pinto Molina et al., 2023, p.15). In addition to data literacy’s links to information literacy, librarians and data archivists are also well-situated to assist with data literacy services as they are highly connected to existing library workflows, such as information discovery, dissemination, publication, and subject-specific services (MacMillan, 2014). Libraries are also experienced in “fostering cross-departmental, cross- campus, etc. communication and collaboration,” which is needed for effective research data management and data literacy education as data needs become ever more interdisciplinary (Koltay, 2017, p.8). Data discovery Bonikowska, Sanmartin and Frenette (2019) found that two core information literacy skills as they relate to data—data discovery and evaluating and ensuring the quality of data and sources—were two of the five competencies that appeared in all five of the frameworks they investigated. Data discovery is the process of finding relevant data to meet a research need (Gregory et al., 2018). Students competent in data discovery can access data from a range of sources rather than using data they collect themselves or that is directly given to them (Ridsdale et al., 2015). However, the term is used inconsistently. Librarians use the term in reference to the information-seeking aspect of finding existing data sources, while statistics and math educators often use the term “discovery” to 3/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 refer to finding patterns, actionable insight or other areas of interest in the data at hand (Wilson et al., 2021; Curley & Peterson, 2022; Hassad, 2020; Roth & Temple, 2014). It is also used in the context of the constructivist approach of student-centered discovery within data education (Dangol & Dasgupta, 2023). The ability to find relevant data is critical to the work of researchers. Much like a literature review, data discovery can give researchers an idea of what others in their field have found and highlight gaps in the information landscape. Finding data to reuse is more efficient than replicating the data collection process, which isn’t feasible for many researchers. Data discovery is also essential for information evaluation, as researchers should be able to trace claims back to the original data, preform their own analysis to confirm claims, and locate and interrogate the accompanying documentation for biases. Despite the importance of data discovery to the quantitative research process, previous scholarship has shown that many researchers struggle with this competency. According to Sun et al. (2024), researchers often turn to data support specialists for data discovery help for both exploratory searches for new data and for known-item searches. Their issues with discovering data lie in a “lack of data search skill, lack of data literacy, and lack of access to data” (Sun et al., 2024, p.8). Most researchers find datasets through their interpersonal connections or through the data’s citation in text-based sources such as articles (Mathiak et al., 2023; Million et al., 2024). If that fails, they turn to open web searches (Sun et al., 2024). In contrast, data librarians and other data support specialists are adept at data discovery and find that data discovery services make up the bulk of their support interactions. Data support specialists are more likely to approach data discovery differently than literature discovery and are more adept at using a variety of sources like search engines, domain repositories, and governmental sources. However, few librarians have the specialized training needed to be confident in their data discovery skills as there is a clear difference in the way data is cataloged, stored, searched for, accessed, and used compared to traditional library materials (Huck, 2020; Million et al., 2024). Evaluation and ensuring quality of data Evaluating and ensuring the quality of data is a critical skill for a data literate population and is another common competency across data literacy models and definitions (Bonikowska et al., 2019). This skill involves critically considering the trustworthiness of data and its sources, identifying errors in data, evaluating if captured data represents the original information correctly, and determining the quality of data and assessments. Data literate people know that when evaluating data, they are evaluating “1) trustworthiness of the measurement 2) trustworthiness of the data processing and 3) trustworthiness of the data integration and visualization” (Koedel et al., 2022, p.1). Like information evaluation, there are several different data quality models that serve as benchmarks for characteristics of high-quality data. The ISO/IEC 25012 Data Quality Model, for example, lists and defines 15 characteristics such as accuracy, credibility, currentness, compliance, and traceability (ISO/IEC 25012, 2008). Some data repositories, such as Kaggle.com, created data quality assessment scores, but their basis for score calculations are often unclear or evaluate aspects of the dataset that have little bearing on the actual quality of the data. For example, having a cover photo for the dataset increases its quality rating on Kaggle.com (Chicco et al., 2025). Furthermore, these data quality models 4/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 are often dependent on which field of research they were created in, which means that there may be elements that are irrelevant when applied from, for example, soil composition data to pharmacology data. Despite these differences, most models agree that high-quality data represents the real-world accurately, and has attributes of “accuracy, correctness, currency, completeness and relevance” (Bertino, 2015, p.19). Evaluating and ensuring quality of data looks different depending on the role of user. Both data creators and data consumers need to actively evaluate and ensure the quality of the data at hand. Data creators have several crucial responsibilities: they must ensure the accuracy of their measurements, critically examine their collection process for potential biases, properly address missing data and outliers, reflexively evaluate their own potential biases, and provide comprehensive, "thick description” or context for their data (Korstjens & Moser, 2018, p. 122). These steps aid in the reproducibility and appropriate reuse of their data. When evaluating data, data consumers must consider the intrinsic data quality and the contextual data quality of the externally produced data (Mahanti, 2019). Intrinsic data quality considers the elements of the data itself, as discussed above, such as completeness, accuracy and consistency. Contextual data quality refers to the users’ own context, such as their research question and purpose for using the data. Much of the library literature discusses the quality of data in terms of research data management and working with data creators to better preserve, describe, store, and share their data (Giarlo, 2013). Evaluating the quality of data and its sources for externally produced data is widely discussed in the library literature, but typically in vague terms. Often, library literature will discuss evaluating data- based claims or conclusions in media and other sources using information literacy frameworks, such as Brungard and Smith (2021). While these skills can certainly be applied to data and data sources, they do not necessarily address how to identify the previously discussed data quality models' attributes of high-quality data. Other library literature stressed the importance of evaluation of data sources but does not directly state what qualities need to be evaluated (Carlson et al., 2015). Others state some characteristics of high-quality data as defined by the data quality models, such as Arellano Douglas et al. (2021), when they provide an evaluation learning objective where “students will critically examine data for accuracy, reliability, bias and context” (p.43). The vagueness about what characterizes high-quality data, and which attributes should be the focus when evaluating data extends outside of the library. Sapp Nelson (2014) highlighted this in their study in which a faculty member “focused on using critical thinking to evaluate the contents of an externally produced data set for quality”, but “did not describe the actual metrics by which an individual evaluates data quality” (p.232). This provides an opportunity for librarians to collaborate with faculty to better understand and teach data evaluation skills. Methodology To create our corpus, we searched 11 journals that were identified by the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE) as publishing research related to statistics education: Technology Innovations in Statistics Education (TISE), Journal for Research in Mathematics Education (JRME), Educational Studies in Mathematics (ESM), Mathematical Thinking and Learning (MTL), International Journal of Mathematical Education in Science and Technology, International Statistical Review (ISR), The American Statistician (TAS), Mathematics Teacher (MT), Teaching Statistics, Journal of Statistics and Data Science Education, Journal of Statistics Education, 5/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 and Statistics Education Research Journal (CAUSE, n.d.). We decided to limit the search to these journals so as to address the landscape of data literacy specifically in statistics education, and not other disciplines, which may cover different competencies of data literacy. Furthermore, articles about data literacy education are not consistently described, making searching a broader corpus difficult and inaccurate. By targeting this curated set of journals that publish articles in statistics education, we also intentionally focus on educational settings that range from primary schools to graduate students. While limiting a scoping review to specific individual journals is not a common practice, it has been done in several fields (Maggio et al., 2021; Logan et al., 2024; Medeiros et al., 2024). We further limited our search to articles published within the last 12 years to evaluate only current literature on the topic. Due to the large volume of articles that mention data literacy in passing, we searched for the term in the title, abstract or keywords of the articles. Additionally, we experimented with search terms that would include articles that addressed the concept of data literacy education. Over 40 different search terms were tested in 12 different search queries in the Scopus database, as it indexed all of the identified journals. The results of each search were reviewed, comparing the total number of results and the relevancy of the articles’ titles and abstracts to the research goal and to the other search queries’ results. This review was done by exporting the results to a spreadsheet, manually scanning the results’ titles and abstracts for relevance to our research question, marking the results, and comparing them to the other searches’ results. The final search string, which is reproduced below was chosen for its adequately scoped results that were highly relevant to the research goal. ISSN ( 1570-1824 OR 2693-9169 OR 10691898 OR 1933-4214 OR 0021-8251 OR 1573-0816 OR 1532-7833 OR 1464-5211 OR 0306-7734 OR 1537-2731 OR 1467-9639 ) AND PUBYEAR > 2012 AND PUBYEAR < 2024 AND TITLE-ABS-KEY ( student* OR literacy OR education OR class OR classroom OR curriculum ) AND ( TITLE ( "data literacy" OR "statistical literacy" OR data ) OR KEY ( "data literacy" OR "statistical literacy" OR data ) Another manual review of the articles was undertaken to remove irrelevant results. Articles that focused on purely pedagogical practices, such as articles detailing the effectiveness of flipped classroom course designs, or articles that focused primarily on discussing mathematical concepts, were removed from the corpus as they did not discuss the data literacy competencies that educators wanted students to learn. Articles that used simulated data rather than real-world data were also removed from the corpus. Simulated data does not provide students with the opportunity to work with several key data literacy competencies, such as evaluating the quality of data and its sources and data collection, and it removes the connection of data from the important real-world contexts. A small number of articles were removed as they either were not research-based articles (e.g., editorials) or their connection to data literacy education was minimal. This left us with a corpus of 260 articles. Criteria Inclusion Exclusion Topic Data literacy education learning outcomes pedagogical strategy focus; mathematical concepts 6/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 without a data literacy education component; Data Context Use of real-world data Use of simulated or synthetic data Publication Date 2012-2024 Articles published outside of the data range The corpus of articles was loaded into the NVivo qualitative data analysis software for analysis. Each of the articles was coded to indicate whether the full-text contained any mention of each of the 27 data literacy competencies mentioned in Bonikowska, Sanmartin and Frenette (2019). (See Appendix A for a list of those 27 competencies.) If an article contained any indication, even in passing, that the author or authors of the article believed that achieving a given competency was a worthwhile outcome of statistics education, that article was coded as mentioning that competency. Results and discussion On our first pass at coding the articles, we identified several codes that could be applied to a near- majority or a majority of the articles, including basic data analysis, data visualization, data interpretation, and data tools. This is expected, as these competencies are traditionally the focus of data or statistics education. However, interdisciplinary competences were also highly represented. Critical thinking was a common theme, which aligns with a shift in statistics education that encourages connecting students to real-world data and its implications (Ferris & Cheng, 2018; Koga, 2022; Ben-Zvi & Garfield, 2004). Communication skills were also highly valued, with slightly less than half of the articles mentioning the importance of students’ abilities to present their findings verbally. Given the ubiquity of these codes and, in many cases, their lack of a clear connection to the aspects of data literacy that are most relevant to the work of libraries and data archives, we chose not to pursue them further, and instead to focus on our analyses on other data literacy competencies. Data discovery Data discovery was only mentioned in 30 of the 260 articles (11.53%). Of those articles, 12 (4.62% of the overall corpus) mentioned data discovery in passing (i.e., mentioning that students were expected to find an outside data source for an assignment). Another 14 articles (5.38%) mentioned data discovery outlined specified data sets or data sources as recommendations for educators to use with their classes. However, 9 of the 35 mentioned sources are no longer widely available for use or are significantly out of date, and another 10 belong to US government agencies that are currently facing mass information suppression. This highlights the issue of providing sources without accompanying skills as sources are prone to disruption, while skills can be more widely applied to various statistical inquiries and can better stand the test of time. Only 4 articles (1.53%) in the corpus discussed relevant skills that students would need to facilitate data discovery outside of classroom-provided materials. One of these articles (Fergusson & Wild, 2021) discussed APIs. While articles about APIs were typically listed as “data collection” or “data tools” 7/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 in the coding, this article prompted students to find their own data sources using APIs and highlighted the importance of combining data from different sources. Another article (Caballer-Tarazona & Coll- Serrano, 2020) featured a learning goal that students “become familiar with an official data base and realize that even if data are available, key skills are required to manage the data and extract and understand the available information” (p.309), which is a critical aspect of data discovery. One article (Çetinkaya-Rundel et al., 2022) provided guidance for student’s data discovery. They stated, “An approach where students are given only guidance, but not a list to pick a dataset from, gives them full control over their project” (p.6), highlighting the importance of data discovery for undertaking the data inquiry process. Their first guideline prompts students to think about their questions and what sort of variables and units would be needed in their desired dataset. They go further to state that their question may not have a readily available dataset, so they may need to revisit the question they are asking until they find “a happy medium” (Çetinkaya-Rundel et al., 2022, p.6). The authors also recommend the services of librarians as they “are helpful in locating data to answer specific questions as well as helping students restate their questions to better match the data available” (Çetinkaya-Rundel et al., 2022, p.6). This absence of focus on data discovery skills is not surprising when considered in the context of existing literature on data discovery. As discussed above, researchers often turn to data support specialists for data discovery assistance (Sun et al. 2024). Researchers rely on interpersonal connections or literature searches for their data needs, and this is replicated in the classroom. Students and researchers would benefit greatly from librarians’ expertise in strategically searching for data. Librarians are well-situated, if not always well-trained, to help with data discovery efforts. Data discovery is information-seeking, and as such it requires parallel skills to traditional information discovery, such as source evaluation, search queries, and knowledge of appropriate databases. Evaluation and ensuring quality of data In the corpus, 51 articles (19.62%) discussed elements of evaluating and ensuring quality of data. It is important to note that we coded articles that discussed evaluating conclusions, analyses, or claims to the code “evaluating decisions and conclusions based on data,” leaving only articles that discussed the quality of data and datasets. Additionally, articles that discussed quality of data in terms of research data management competencies were coded to other competency codes such as data duration and reuse. Fourteen of these articles (5.38%) mentioned this competency in passing, vaguely referring to the importance of evaluating data but without any specific discussion of what that entails from either the position of a data creator or a data collector. Fifteen articles (5.76%) discussed ensuring the quality of one’s own data, typically in reference to data collection methods (Frölich & Schellhammer, 2022; Zhu et al., 2013), measurement (Casleton et al., 2014), variability (Roth & Temple, 2014), sampling, and evaluating models to see if they accurately represented real-world phenomenon (Fleischer et al., 2022). Very few of the articles discussed data quality in terms of the data quality models. Bilgin et al. (2022) stands out, as they discuss the use of a quality manual and documenting the project in terms of Cross Industry Standard Process for Data Mining (CRISP-DM). Zhu et al. (2013) did not directly relate back to a specific data quality model, but listed in-depth, systematic quality control measures that would match with previously discussed data quality attributes such as critical examination of their 8/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 collection process for potential biases, properly addressing missing data and outliers, precision in measurements, and detail reporting of context. The remaining 26 articles (10%) discussed evaluating the quality of externally produced data. Most of these articles focused on asking critical questions of the data such as who created the data, how they gathered it, and its original purpose, such as Delport (2023) with their use of “worry questions,” and Lee et al. (2022) with their discussion of the issue of bias, both in what is represented in the data and what is not represented in the data. This focus on bias relates to a broader competency of critical thinking, which was far better represented in the data literacy literature, with 138 (53.01%) articles covering the topic. While evaluating and ensuring quality of data and sources certainly requires a level of critical thinking, it is more specific in the goals of the critical questioning than critical thinking alone does. It is not enough to ask the questions like, “Who made this?” and “What sort of biases could be present?.” To truly evaluate the quality of data and its sources, one must be able to find the answers to those questions and, potentially, know how to compensate for the weaknesses in data or its sources. However, directions for asking the questions are rarely followed with instructions on how to find the answers to such questions. Few of the articles coded as “evaluating and ensuring quality of data and sources,” discussed how students could find information to answer the questions they were asking of the data source outside of an accompanying data dictionary. While a data dictionary would be important for this task, much of the available data does not come with a data dictionary or codebook. Lee et al (2022) suggests that it may “be necessary to reach out external stakeholders or experts to find additional information about the data context” (p.14) to answer questions about the quality of data. Other articles suggested comparing findings with other sources, which is an essential skill in both data literacy and information literacy. However, there is a gap in data discovery skills, as previously discussed. Without strong data discovery skills, it would be difficult to find an alternative data source that matched the important features (i.e., research focus, method, measurements, categories, etc.) sufficiently to make a meaningful comparison. Besides these suggestions, none of the articles prompted students to do their own research on the source of the data, instead relying on educators or the data providers themselves to provide all the necessary context for the data. Overall, the articles in the corpus stressed the importance of ensuring and evaluating the quality of found data but did not discuss how to teach practical skills for doing so. Çetinkaya-Rundel et al. (2022) provide the most practical directions for evaluating found datasets in their guidelines for student- selected datasets. They set basic standards in terms of number of variables and observations that need to be present in a dataset to account for confounding variables. They also require that students use data that includes a “comprehensive” data dictionary, stating, “Without these, it is impossible for students to evaluate the reliability, validity, and ethical considerations of the data for their projects” (Çetinkaya-Rundel et al., 2022, p. 6). The article warns students to be selective about using data from aggregators, citing concerns over varying states of documentation for and the use of sample data analyses in the datasets. Still, this article, like the others in the corpus, did not discuss the characteristics of high-quality found data that are mentioned in data quality standards, such as consistent, unambiguous, and current (Mahanti, 2019; ISO/IEC, 2008). Some mention these characteristics in passing (e.g., Jones, 2020), but there is not a clear discussion of how students can 9/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1- 47. DOI: https://doi.org/10.29173/iq1156 spot the presence or lack thereof of these characteristics. While librarians can apply information evaluation skills to assist in this area, some consensus would be needed on what metrics and attributes qualify data as high-quality. Reproducibility: A disconnect between librarians and statisticians Reproducibility and reproducible research workflows were common themes in these articles. In the included articles, reproducibility typically means computational reproducibility: “A reproducible analysis is one that can be rerun (potentially years later, or by a different person) with the same data to produce exactly the same result” (McNamara, 2019, p. 381). While computational reproducibility is important, it is not the only form of reproducibility. Some other fields strongly emphasize other forms of reproducibility, even to the point of defining “reproducibility” differently. For example, Plesser (2018) compiled definitions of reproducibility and the related concepts of “repeatability” and “replicability” from a number of scientific fields, including geophysics, chemistry and computer science. As Plesser noted, the idea of reproducibility inherent in “computational reproducibility” “is at odds with the terminology long established in experimental sciences” (Plesser 2018, 1), which uses “repeatability” to describe the condition where the same procedure run on the same equipment under the same conditions produces the same results. In those fields, “reproducibility” means the ability of a different research group, using different equipment, to produce the same results. To make the matter even more confusing, some fields use an additional term, “replicability,” which, depending on the field, can mean something closer to “computational reproducibility” or something closer to the experimental sciences’ definition of “reproducibility” (Plesser 2018). Existing models for data literacy are more closely aligned with the experimental sciences’ definition of “reproducibility” than with the concept of computational reproducibility. No competencies that directly map to the concept of computational reproducibility appear in either of the compilations of data literacy competencies that were discussed in the literature review of this article (Downes 2023 and Bonikowska et al. 2019). Instead, the data literacy models that were compiled in these two reviews emphasize competencies such as data curation, data preservation, and data sharing, which are necessary to allow for research results to be reproduced by other research groups. Yet very few of the articles included in this analysis mentioned the data literacy competencies such as these that would allow researchers to make sure that other researchers outside of their own circles, or that the researchers themselves in the long-range future, could access and use the data necessary to reproduce their research. For example, data preservation (ensuring that the data is preserved in its original state for the duration of the student’s or researcher’s period of analysis) and data duration and reuse (ensuring that data is preserved in its original state indefinitely, and in such a way that it can be made available to and reused by other researchers) are rarely discussed explicitly using those terms, although both aspects are inherent in the experimental sciences’ definition of reproducible research. For the purposes of this project, we assumed that, unless the context clearly indicated otherwise, any mention of “reproducibility” in these articles included data preservation as one of the implicit goals. This assumption is due to the typical structure of the projects discussed in the articles, where students were expected to turn in their original data files, along with any code that was used to manipulate or analyze the data, allowing the instructor to reproduce the students’ entire data processing and 10/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 analysis procedure. With this generous definition of data preservation, 66 articles (25.38%) were coded as mentioning data preservation. However, these articles rarely explicitly call out “not altering the original data file” as one of the benefits of a reproducible analysis. Also, some of the articles discuss reproducible research workflows—being able to run the exact same analysis steps on a different dataset, rather than on the same dataset. Where it was clear that reproducible workflows rather than reproducible analyses were being discussed, the article was not coded as “data preservation.” Given all of these caveats, it is difficult to say precisely how frequently students are explicitly being taught about the importance of preserving a copy of their original data file. Conversely, we did not assume that “reproducibility” referred to data duration and reuse and data sharing unless those competencies were explicitly mentioned. This assumption contributed to a much lower number of articles being coded to the “data duration and reuse” and “data sharing” competencies: 16 (6.15%) and 10 (3.85%), respectively. The concept of metadata creation also appeared infrequently, in 11 articles (4.23%), although typically in the guise of “codebooks” and “data dictionaries”: forms of metadata that are very useful for accurately interpreting a given dataset, but that are less helpful for dataset discovery. Data citation was mentioned in only three articles (1.15%), and always in passing. A lack of data citation skills is likely to hamper reproducibility, given that many datasets that may be used in research have contractual or ethical restrictions that prevent them from being freely redistributed by the researchers who use them. If students do not learn how to cite data properly, people in the future who wish to replicate their analysis may be unable to identify and access the specific dataset that was used. Similarly, data sharing was mentioned in only 10 articles (3.85%), again usually without any sort of detail about issues to be considered or specific tasks to be completed in order to share data ethically and effectively. A good example of a typical discussion of data sharing in this corpus can be found in Donoghue, Voytek, and Ellis’s article “Teaching Creative and Practical Data Science at Scale,” (2021), which focuses on the skills that students need to learn to be effective data science professionals (see pp. S33-S34). The authors list many specific practices and skills that students must master to be able to carry out reproducible research, but most of these skills are related to the code that implements the analysis rather than the data that is being analyzed. The code must be “well-organized, documented, and tested;” it must be “understandable by other analysts.” The data, however, only needs to be “stor[ed] . . . in a consistent manner.” All of the other skills and practices necessary for ethical and effective data sharing go unmentioned. Conclusion Although this study, which only examined published journal articles, demonstrated which data literacy competencies are well-covered in the statistics education literature and which are not, it does not and cannot explain why the missing competencies are not covered. Future research in this area should draw on a wider range of sources, in particular conversations with statistics educators about their views of the missing data literacy competencies. Do statistics educators not think these competencies are important? Do they not feel equipped to teach them themselves? Do they believe that they are 11/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 being covered in classes outside of statistics? Without answers to questions such as these, it is not clear how librarians and other information professionals can best position ourselves to help. Data librarians and research data management specialists are well-equipped to teach the data literacy skills that are apparently not being covered in statistics education, as these skills are of deep interest to these professions, but becoming empowered to teach these skills to students in statistics classes will require collaboration with statistics educators and a richer understanding of statistics educators’ perspectives on data literacy. References list Ben-Zvi, D., & Garfield, J. (Eds.). (2004). The Challenge of Developing Statistical Literacy, Reasoning and Thinking. Springer Netherlands. https://doi.org/10.1007/1-4020-2278-6 Bertino, E. (2015). Data trustworthiness—approaches and research challenges. In Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (Vol. 8872, pp. 17–25). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-17016-9_2 Bilgin, A. A. B., Powell, A., & Richards, D. (2022). Work integrated learning in data science and a proposed assessment framework. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.26 Bonikowska, A., Sanmartin, C., & Frenette, M. (2019, August 14). Data literacy: what is it and how to measure it in the public service. https://www150.statcan.gc.ca/n1/en/pub/11-633- x/11-633-x2019003-eng.pdf Brungard, A., & Smith, L. (2021). A data discovery project: seeking truth in a post-truth world. In J. Bauder (Eds.), Data Literacy in Academic Libraries. American Library Association. Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. portal: Libraries and the Academy, 11(2), 629-657. http://dx.doi.org/10.1353/pla.2011.0022 Caballer-Tarazona, M., & Coll-Serrano, V. (2020). The raising factor, that great unknown. A guided activity for undergraduate students. Journal of Statistics Education, 28(3), 304–315. https://doi.org/10.1080/10691898.2020.1832006 Casleton, E., Beyler, A., Genschel, U., & Wilson, A. (2014). A pilot study teaching metrology in an introductory statistics course. Journal of Statistics Education, 22(3), 1. https://doi.org/10.1080/10691898.2014.11889710 Çetinkaya-Rundel, M., Dogucu, M., & Rummerfield, W. (2022). The 5ws and 1h of term projects in the introductory data science classroom. Statistics Education Research Journal, 21(2), 1– 19. https://doi.org/10.52041/serj.v21i2.37 12/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Chicco, D., Fabris, A., & Jurman, G. (2025). The Venus score for the assessment of the quality and trustworthiness of biomedical datasets. BioData Mining, 18(1), 1–31. https://doi.org/10.1186/s13040-024-00412-x Consortium for the Advancement of Undergraduate Statistics Education (CAUSE) (n.d.). Journals publishing research in statistics education. https://www.causeweb.org/cause/research/journals Curley, B., & Peterson, A. (2022). A fresh shot at statistics in the classroom: three perspectives using world cup soccer player data. Journal of Statistics and Data Science Education, 30(1), 86–98. https://doi.org/10.1080/26939169.2021.2008283 Dangol, A., & Dasgupta, S. (2023). Constructionist approaches to critical data literacy: A review. Proceedings of the 22nd Annual ACM Interaction Design and Children Conference, 112– 123. https://doi.org/10.1145/3585088.3589367 Delport, D. H. (2023). The development of statistical literacy among students: Analyzing messages in media articles with Gal’s worry questions. Teaching Statistics, 45(2), 61–68. https://doi.org/10.1111/test.12308 Donoghue, T., Voytek, B., & Ellis, S. E. (2021). Teaching creative and practical data science at scale. Journal of Statistics and Data Science Education, 29(S1), S27–S39. https://doi.org/10.1080/10691898.2020.1860725 Downes, S. (2023). Three frameworks for data literacy. In D. G. Sampson, D. Ifenthaler, D., & P. Isaías (Eds.), Proceedings of the 20th International Conference on Cognition and Exploratory Learning in the Digital Age (107-115). IADIS Press. https://files.eric.ed.gov/fulltext/ED636095.pdf Ferris, M., & Cheng, S. (2018). Using twitter to energize the introductory statistics class. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111032036 Fleischer, Y., Biehler, R., & Schulte, C. (2022). Teaching and learning data-driven machine learning with educationally designed jupyter notebooks. Statistics Education Research Journal, 21(2), 1–25. https://doi.org/10.52041/serj.v21i2.61 Frölich, N., & Schellhammer, K. S. (2022). Questionnaire design and sampling procedures for business and economics students: A research-oriented, hands-on course. International Journal of Mathematical Education in Science and Technology, 0(0), 1–19. https://doi.org/10.1080/0020739X.2022.2056722 Giarlo, M. J. (2013). Academic libraries as data quality hubs. Journal of Librarianship and Scholarly Communication, 1(3). https://doi.org/10.7710/2162-3309.1059 Gregory, K., Khalsa, S. J., Michener, W. K., Psomopoulos, F. E., de Waard, A., & Wu, M. (2018). Eleven quick tips for finding research data. PLoS Computational Biology, 14(4). https://doi.org/10.1371/journal.pcbi.1006038 13/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Hassad, R. A. (2020). A Foundation for Inductive Reasoning in Harnessing the Potential of Big Data. Statistics Education Research Journal, 19(1), 238–258. https://doi.org/10.52041/serj.v19i1.133 Huck, J. (2020). Identifying, accessing and evaluating data: finding and accessing data can be problematic, but many of the skills used in traditional reference can be applied to data discovery. Information Outlook, 24(1), 4-6. https://scholarworks.sjsu.edu/sla_io_2020/1 ISO/IEC. (2008). Software Engineering—Software Product Quality Requirements and Evaluation (SQuaRE)—Data Quality Model (25012:2008). https://www.iso.org/standard/35736.html Jones, J. D. (2022). Using school mathematics to develop students’ data literacy skills. Mathematics Teacher: Learning and Teaching PK-12, 115(8), 576–581. https://doi.org/10.5951/MTLT.2021.0239 Koedel, U., Schuetze, C., Fischer, P., Bussmann, I., Sauer, P. K., Nixdorf, E., Kalbacher, T., Wichert, V., Rechid, D., Bouwer, L. M., & Dietrich, P. (2022). Challenges in the evaluation of observational data trustworthiness from a data producers viewpoint (FAIR+). Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.772666 Koga, S. (2022). Characteristics of statistical literacy skills from the perspective of critical thinking. Teaching Statistics, 44(2), 59–67. https://doi.org/10.1111/test.12302 Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. The European Journal of General Practice, 24(1), 120– 124. https://doi.org/10.1080/13814788.2017.1375092 Lee, H. S., Mojica, G. F., Thrasher, E. P., & Baumgartner, P. (2022). Investigating data like a data scientist: key practices and processes. Statistics Education Research Journal, 21(2), 1– 23. https://doi.org/10.52041/serj.v21i2.41 Logan, J., Webb, J., Singh, N. K., Tanner, N., Barrett, K., Wall, M., Walsh, B., & Ayala, A. P. (2024). Scoping review search practices in the social sciences: A scoping review. Research Synthesis Methods, 15(6), 950–963. https://doi.org/10.1002/jrsm.1742 Mahanti, R. (2019). Data Quality: Dimensions, Measurement, Strategy, Management, and Governance. Quality Press. http://ebookcentral.proquest.com/lib/grinnell- ebooks/detail.action?docID=6262212 Maggio, L. A., Larsen, K., Thomas, A., Costello, J. A., & Artino Jr., A. R. (2021). Scoping reviews in medical education: A scoping review. Medical Education, 55(6), 689–700. https://doi.org/10.1111/medu.14431 Mathiak, B., Juty, N., Bardi, A., Colomb, J., & Kraker, P. (2023). What are researchers’ needs in data discovery? Analysis and ranking of a large-scale collection of crowdsourced use cases. Data Science Journal, 22(1). https://doi.org/10.5334/dsj-2023-003 14/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 McNamara, A. (2019). Key attributes of a modern statistical computing tool. The American Statistician, 73(4), 375–384. https://doi.org/10.1080/00031305.2018.1482784 Medeiros, P., Shetty, J., Lamaj, L., Cunningham, J., Wanigaratne, S., Guttmann, A., & Cohen, E. (2024). Reported community engagement in health equity research published in high- impact medical journals: A scoping review. https://doi.org/10.1136/bmjopen-2024- 084952 Million, A. J., York, J., Lafia, S., & Hemphill, L. (2024). Data, not documents: Moving beyond theories of information-seeking behavior to advance data discovery. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.24962 Plesser, H.E. (2018). Reproducibility vs. replicability: A brief history of a confused terminology. Frontiers in Neuroinformatics 11 (76). https://doi.org/10.3389/fninf.2017.00076 Prado, J.C., & Marzal, M.A. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri 63 (2): 123–134. https://doi.org/10.1515/libri- 2013-0010 Roth, W.-M., & Temple, S. (2014). On understanding variability in data: A study of graph interpretation in an advanced experimental biology laboratory. Educational Studies in Mathematics, 86(3), 359–376. https://doi.org/10.1007/s10649-014-9535-5 Schield, M. (2004). Information literacy, statistical literacy and data literacy. IASSIST Quarterly Summer/Fall: 6–11. https://doi.org/10.29173/iq790 Sun, G., Friedrich, T., Gregory, K., & Mathiak, B. (2024). Supporting data discovery: comparing perspectives of support specialists and researchers. Data Science Journal, 23(1). https://doi.org/10.5334/dsj-2024-048 Towse, J., Davies, R., Ball, E., James, R., Gooding, B., & Ivory, M. (2022). LUSTRE: an online data management and student project resource. Journal of Statistics and Data Science Education, 30(3), 266–273. https://doi.org/10.1080/26939169.2022.2118645 Wilkerson, M. H., Lanouette, K., & Shareff, R. L. (2022). Exploring variability during data preparation: A way to connect data, chance, and context when working with complex public datasets. Mathematical Thinking and Learning, 24(4), 312–330. https://doi.org/10.1080/10986065.2021.1922838 Wilson, M., Ross, A., & Casey, S. (2021). A classroom-ready activity on educational disparities in the United States. Teaching Statistics, 43(S1), S93–S97. https://doi.org/10.1111/test.12252 Zhu, Y., Hernandez, L. M., Mueller, P., Dong, Y., & Forman, M. R. (2013). Data acquisition and preprocessing in studies on humans: what is not taught in statistics classes? The American Statistician, 67(4), 235–241. https://doi.org/10.1080/00031305.2013.842498 15/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Appendix A: Data Literacy Definitions The codes used for this review were collected from Bonikowska et al., 2019, which compared competencies of data literacy found in Data to the People (2018) 1 ; Grillenberger & Romeike (2018) 2 ; Ridsdale et al. (2015)3; Sternkopf & Mueller (2018)4; and Wolff et al. (2016)5. Below are the definitions of each competency that we synthesized from the five articles. Competencies Definitions Basic data analysis (select appropriate tool/algorithms/analysis methods for data, knowledge and use of basic summary/descriptive statistics) Basic data analysis involves developing and executing plans to examine data using appropriate tools, algorithms and analysis methods including descriptive statistics, hypothesis testing, linear regression, etc. 1,2,3,4,5 Critical thinking (aware of high-level issues associated with data, thinks critically when working with data) Critical thinking involves being aware of high-level issues and challenges associated with data while applying thoughtful consideration when working with it. 2,3 Data culture (psychological barriers, attitudes, etc towards data) Data culture refers to the recognition of data's importance and the fostering of an environment that promotes critical use of data for learning, research, and decision-making. It involves overcoming psychological barriers related to data, understanding its potential as an enabler for progress, and securing support from management for data initiatives and resources. 3,4 Data collection (gathering data, structure gathered data, critically evaluate the collection process) Data collection encompasses the process of gathering information in various formats and complexities to support specific needs. It involves selecting appropriate methods, implementing algorithms, and considering ethical issues and privacy impacts. 1,2,3,5 Data conversion (from format to format) Data conversion is the ability to transform data from one format or file type to another, requiring knowledge of different data types and conversion methods. 1,3,4,5 Data ethics (security, privacy issues) Data ethics involves understanding and addressing the moral implications of collecting, analyzing, and using data. It requires considering privacy concerns, potential biases, and the societal impact of data-driven decisions. Advanced practitioners can develop ethical frameworks, guide others in ethical data practices, and advocate for responsible data use within organizations. 2,3,4,5 16/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Data discovery (ability to find and access data, connect data from different sources, identify useful data) Data discovery is the ability to find, access, and identify relevant data from various sources. It progresses from using basic search engines to understanding and selecting from a wide range of data sources, including specialized data portals. Advanced skills include assisting others in locating data and formulating assessment criteria for selecting the most relevant data sources for specific informational needs. 1,2,3,4,5 Data driven decision making (Prioritizes information garnered from data, converts data into actionable information Weighs the merit and impacts of possible solutions/decisions, Implements decisions/solutions) Data-driven decision making (DDDM) is the process of using data to inform and guide strategic choices. It involves analyzing relevant data, converting it into actionable insights, and weighing potential outcomes to make informed decisions. Those skilled in DDDM can also communicate and defend their data-based decisions. 1,3,5 Data duration and re- use (structure data in suitable way for storage and other's re-use, curation requirements) Data duration and re-use refers to the process of structuring and storing data in a way that facilitates long-term preservation and future utilization by others. This competency involves assessing curation requirements, implementing appropriate storage methods, and ensuring data accessibility while considering ethical and security concerns. 1,2,3 Data interpretation (understanding data, read and understand charts & tables, find key points and relationships in data) Data interpretation is the ability to understand and extract meaning from data outputs such as analyses and visualizations. It involves identifying key points of interest, recognizing relationships within data, and critically assessing the implications of data outputs. 1,2,3,4,5 Data management and organization (store and organize data appropriately for the analysis) Data management covers the practices of organizing and storing data for the length of the analysis process. 1,2,3 Data manipulation (data cleaning, knowledge that most data is not clean, combine data, decide when it is appropriate to combine Data manipulation involves transforming, cleaning, and restructuring data to make it suitable for analysis. At a basic level, data users know that most data are not clean and that cleaning the data is necessary for analysis. Skills range from basic sorting and filtering to advanced techniques like appropriately removing outliers and anomalies and deciding when it is appropriate to combine data. 1,2,3,4,5 17/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 data, remove outliers and anomalies) Data preservation (decide which data to keep or delete, identify appropriate ways to store data) Data preservation encompasses determining which data to retain, who should have access, how to ensure the integrity of the data, and how to ethically handle data deletion. Effective data preservation ensures data validity, addresses ethical considerations, and maintains data accessibility over time. 2,3 Data sharing (decide whom to share data with from a legal or ethical perspective, prepare data for sharing) Data sharing involves the practice of making data available to others, both within and outside an organization. It requires understanding of data formats, sharing platforms, and relevant legal and ethical considerations. 2,3 Data tools (knowledge of and ability to use tools to collect, store, clean, organize, or analyze data, the ability to choose appropriate tool for the task) Data Tools are software applications and techniques used for gathering, structuring, and analyzing data. They encompass a range of functionalities, from selecting suitable sensors for data collection and implementing algorithms to download data from web APIs, to applying various analysis techniques and visualization methods. Those literate in Data Tools have the ability to choose the appropriate tool for the task at hand. 2,3,5 Data visualization (create meaningful graphs and charts, choose appropriate visualizations for the data or analysis) Data visualization is the skill of creating meaningful graphical representation of data to facilitate understanding and insight generation. Those literate in Data Visualization not only make charts and graphs but also know the appropriate type of visualization for the data at hand. 1,2,3,4 Data citation (Knowledge of widely accepted data citation methods Creates correct citations for secondary data sets) Data citation is the knowledge and application of widely-accepted methods for crediting secondary data sets. It involves creating correct citations for data sources, ensuring proper attribution and enabling others to locate and verify the data used in analyses or research. 3 Develop hypotheses (ask questions that can be tested, predict the outcomes of data analysis) The ability to develop hypotheses includes being able to ask questions that can be tested and make informed predictions on the outcome of the data analysis. 5 Evaluating and ensuring quality of data and sources (Critically consider the Evaluating and ensuring quality of data and its sources involves critically assessing the trustworthiness, accuracy, and reliability of data and its origins. This process ranges from identifying errors or problems in datasets to critically evaluating data collection methods, sources, and 18/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 trustworthiness of data, identify errors in data, evaluate if captured data represents original information correctly, quality assessment and data checking) potential biases. It also encompasses the ability to verify data quality through multiple layers of checking and connect data from different sources, ultimately ensuring that the data used is representative and suitable for analysis and decision-making purposes. 1,2,3,4,5 Evaluating decisions/conclusions based on data (collect follow-up data, compare with other findings) Evaluating decisions/conclusions based on data involves assessing the effectiveness of actions or solutions by analyzing follow-up data and comparing results with other findings. This process includes collecting relevant data from various sources, conducting thorough analysis, and using the insights gained to either validate original conclusions or implement new decisions. The ultimate goal is to ensure that decisions are continually refined and improved based on empirical evidence, fostering a culture of data-driven decision-making and continuous improvement within organizations. 1,3 Identifying problems using data (knowledge of which questions can be answered by the data) Data literate people should be able to identify and describe problems in practical situations using a range of data sources. Questions should be formulated precisely and target-orientated to find meaningful answers. 1,3,4,5 Metadata creation and use (apply metadata to datasets such as descriptors) Metadata creation and use refers to understanding of what metadata associated with data sources are, why such descriptors are important, and the ability to create and assign appropriate metadata descriptors to original data sources. 1,3 Presenting data verbally (data storytelling, describing key findings, communicate with others about findings) Presenting data verbally involves clearly and coherently describing key insights, datasets, and visualizations in a way that aligns with the audience's needs and familiarity with the subject. This skill progresses from explaining simple data points to effectively using narratives, visualizations, and storytelling to communicate complex data in broader contexts. 1,3,4 Undertake data inquiry process Undertaking a data inquiry process involves a systematic approach to exploring and analyzing data to answer specific questions or solve problems. It is often done through the PPDAC model: Problem, Plan, Data, Analysis and Conclusion (Wolff et al., 2016).5 Work with large data sets Working with large data sets refers to the ability to effectively handle, process, and analyze substantial volumes of data. This can include datasets that are voluptuous and complex like Big Data, datasets that 19/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 require multiple tools to handle effectively, or that combine diverse data types. 5 Knowledge and understanding of data, its uses and applications Data literacy involves understanding the nature of data, its various forms, and how it is produced. A data-literate individual should be aware of data's role and influence in society across diverse settings, as well as the ethical considerations associated with its use. 20/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Appendix B: Article Corpus Abel, T., & Poling, L. (2015). Hold my calls: An activity for introducing the statistical process. Teaching Statistics, 37(3), 96–103. https://doi.org/10.1111/test.12082 Ainley, J., Gould, R., & Pratt, D. (2015). Learning to reason from samples: Commentary from the perspectives of task design and the emergence of “big data.” Educational Studies in Mathematics, 88(3), 405–412. https://doi.org/10.1007/s10649-015-9592-4 Ainley, J., & Pratt, D. (2017). Computational Modelling and Children’s Expressions of Signal and Noise. Statistics Education Research Journal, 16(2), 15–36. https://doi.org/10.52041/serj.v16i2.183 Amdat, W. C. (2021). The Chicago Hardship Index: An Introduction to Urban Inequity. Journal of Statistics and Data Science Education, 29(3), 328–336. https://doi.org/10.1080/26939169.2021.1994489 Aridor, K., & Ben-Zvi, D. (2017). The Co-Emergence Of Aggregate And Modelling Reasoning. Statistics Education Research Journal, 16(2), 38–63. https://doi.org/10.52041/serj.v16i2.184 Arnold, P. (2017). Statistical Literacy in Public Debate—Examples from the Uk 2015 General Election. Statistics Education Research Journal, 16(1), 217–227. https://doi.org/10.52041/serj.v16i1.225 Baglin, J., & Da Costa, C. (2013). Comparing Training Approaches for Technological Skill Development in Introductory Statistics Courses. Technology Innovations in Statistics Education, 7(1). https://doi.org/10.5070/T571014007 Baldi, B., & Utts, J. (2015). What Your Future Doctor Should Know About Statistics: Must-Include Topics for Introductory Undergraduate Biostatistics. The American Statistician, 69(3), 231– 240. https://doi.org/10.1080/00031305.2015.1048903 Bargagliotti, A., Arnold, P., & Franklin, C. (2021). GAISE II: Bringing Data into Classrooms. Mathematics Teacher: Learning and Teaching PK-12, 114(6), 424–435. https://doi.org/10.5951/MTLT.2020.0343 Bargagliotti, A., Binder, W., Blakesley, L., Eusufzai, Z., Fitzpatrick, B., Ford, M., Huchting, K., Larson, S., Miric, N., Rovetti, R., Seal, K., & Zachariah, T. (2020). Undergraduate Learning Outcomes for Achieving Data Acumen. Journal of Statistics Education, 28(2), 197–211. https://doi.org/10.1080/10691898.2020.1776653 Bargagliotti, A. E., & Anderson, C. R. (2017). Using Learning Trajectories for Teacher Learning to Structure Professional Development. Mathematical Thinking and Learning, 19(4), 237–259. https://doi.org/10.1080/10986065.2017.1365222 Baumer, B. (2015). A Data Science Course for Undergraduates: Thinking With Data. The American Statistician, 69(4), 334–342. https://doi.org/10.1080/00031305.2015.1081105 21/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., & Horton, N. J. (2014). R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics. Technology Innovations in Statistics Education, 8(1). https://doi.org/10.5070/T581020118 Baumer, B. S. (2018). Lessons From Between the White Lines for Isolated Data Scientists. The American Statistician, 72(1), 66–71. https://doi.org/10.1080/00031305.2017.1375985 Baumer, B. S., Garcia, R. L., Kim, A. Y., Kinnaird, K. M., & Ott, M. Q. (2022). Integrating Data Science Ethics Into an Undergraduate Major: A Case Study. Journal of Statistics and Data Science Education, 30(1), 15–28. https://doi.org/10.1080/26939169.2022.2038041 Beckman, M. D., Çetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., & Tackett, M. (2021). Implementing Version Control With Git and GitHub as a Learning Objective in Statistics and Data Science Courses. Journal of Statistics and Data Science Education, 29(S1), S132–S144. https://doi.org/10.1080/10691898.2020.1848485 Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393–427. https://doi.org/10.1080/0020739X.2016.1254296 Berg, A., & Hawila, N. (2021). Some teaching resources using R with illustrative examples exploring COVID-19 data. Teaching Statistics, 43(S1), S98–S109. https://doi.org/10.1111/test.12258 Biehler, R., & Fleischer, Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. Teaching Statistics, 43, S133–S142. https://doi.org/10.1111/test.12279 Biehler, R., Frischemeier, D., & Podworny, S. (2017). Elementary Preservice Teachers´ Reasoning About Modeling a “Family Factory” with Tinkerplots – a Pilot Study. Statistics Education Research Journal, 16(2), 244–286. https://doi.org/10.52041/serj.v16i2.192 Bilgin, A. A. B., Date-Huxtable, E., Coady, C., Geiger, V., Cavanagh, M., Mulligan, J., & Petocz, P. (2017). Opening Real Science: Evaluation of an Online Module on Statistical Literacy for Pre- Service Primary Teachers. Statistics Education Research Journal, 16(1), 120–138. https://doi.org/10.52041/serj.v16i1.220 Bilgin, A. A. B., Powell, A., & Richards, D. (2022). Work Integrated Learning In Data Science And A Proposed Assessment Framework. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.26 Boehm, F. J., & Hanlon, B. M. (2021). What Is Happening on Twitter? A Framework for Student Research Projects With Tweets. Journal of Statistics and Data Science Education, 29(S1), S95–S102. https://doi.org/10.1080/10691898.2020.1848486 22/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Boenig-Liptsin, M., Tanweer, A., & Edmundson, A. (2022). Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice. Journal of Statistics and Data Science Education, 30(3), 228–240. https://doi.org/10.1080/26939169.2022.2089411 Bolch, C. A., & Crippen, K. J. (2022). Data Scientists’ Epistemic Thinking for Creating and Interpreting Visualizations. Statistics Education Research Journal, 21(2), 1–25. https://doi.org/10.52041/serj.v21i2.21 Bradley, S. (2015). Handwriting and Gender: A multi-use data set. Journal of Statistics Education, 23(1), 1. https://doi.org/10.1080/10691898.2015.11889721 Brearley, A. M., Bigelow, C., Poisson, L. M., Grambow, S. C., & Nowacki, A. S. (2018). The TSHS Resources Portal: A Source of Real and Relevant Data for Teaching Statistics in the Health Sciences. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111034506 Broatch, J. E., Dietrich, S., & Goelman, D. (2019). Introducing Data Science Techniques by Connecting Database Concepts and dplyr. Journal of Statistics Education, 27(3), 147–153. https://doi.org/10.1080/10691898.2019.1647768 Brown, M. (2017). Making students part of the dataset: A model for statistical enquiry in social issues. Teaching Statistics, 39(3), 79–83. https://doi.org/10.1111/test.12131 Budgett, S., Pfannkuch, M., Regan, M., & Wild, C. J. (2013). Dynamic Visualizations and the Randomization Test. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013889 Budgett, S., & Rose, D. (2017). Developing Statistical Literacy in the Final School Year. Statistics Education Research Journal, 16(1), 139–162. https://doi.org/10.52041/serj.v16i1.221 Burckhardt, P., Nugent, R., & Genovese, C. R. (2021). Teaching Statistical Concepts and Modern Data Analysis With a Computing-Integrated Learning Environment. Journal of Statistics and Data Science Education, 29(S1), S61–S73. https://doi.org/10.1080/10691898.2020.1854637 Burr, W., Chevalier, F., Collins, C., Gibbs, A. L., Ng, R., & Wild, C. J. (2021). Computational skills by stealth in introductory data science teaching. Teaching Statistics, 43(S1), S34–S51. https://doi.org/10.1111/test.12277 Büscher, C. (2022). Design Principles for Developing Statistical Literacy in Middle Schools. Statistics Education Research Journal, 21(1), 1–16. https://doi.org/10.52041/serj.v21i1.80 Caballer-Tarazona, M., & Coll-Serrano, V. (2020). The Raising Factor, That Great Unknown. A Guided Activity for Undergraduate Students. Journal of Statistics Education, 28(3), 304–315. https://doi.org/10.1080/10691898.2020.1832006 23/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Callingham, R. (2011). Assessing Statistical Understanding in Middle Schools: Emerging Issues in a Technology-Rich Environment. Technology Innovations in Statistics Education, 5(1). https://doi.org/10.5070/T551000044 Callingham, R., & Watson, J. M. (2017). The Development of Statistical Literacy at School. Statistics Education Research Journal, 16(1), 181–201. https://doi.org/10.52041/serj.v16i1.223 Cameron, C., Iosua, E., Parry, M., Richards, R., & Jaye, C. (2017). More Than Just Numbers: Challenges for Professional Statisticians. Statistics Education Research Journal, 16(2), 362– 375. https://doi.org/10.52041/serj.v16i2.196 Carter, J., Brown, M., & Simpson, K. (2017). From The Classroom To The Workplace: How Social Science Students Are Learning To Do Data Analysis For Real. Statistics Education Research Journal, 16(1), 80–101. Carver, R. H. (2011). Introductory Statistics Unconstrained by Computability: A New Cobb Salad. Technology Innovations in Statistics Education, 5(1). https://doi.org/10.5070/T551000043 Casas-Rosal, J. C., Caridad Y Ocerín, J. M., Núñez-Tabales, J. M., & León-Mantero, C. (2019). Teaching statistics through the Real Estate Data Analyzer software. Teaching Statistics, 41(2), 58–64. https://doi.org/10.1111/test.12183 Casement, C. J., & McSweeney, L. A. (2022). NormalityAssessment: An Interactive Classroom Tool for Testing Normality Visually. Technology Innovations in Statistics Education, 14(1). https://doi.org/10.5070/T514156556 Casey, S. A., Albert, J., & Ross, A. (2018). Developing Knowledge for Teaching Graphing of Bivariate Categorical Data. Journal of Statistics Education, 26(3), 197–213. https://doi.org/10.1080/10691898.2018.1540915 Casleton, E., Beyler, A., Genschel, U., & Wilson, A. (2014). A Pilot Study Teaching Metrology in an Introductory Statistics Course. Journal of Statistics Education, 22(3), 1. https://doi.org/10.1080/10691898.2014.11889710 Çetinkaya-Rundel, M., Dogucu, M., & Rummerfield, W. (2022). The 5ws And 1h Of Term Projects In The Introductory Data Science Classroom. Statistics Education Research Journal, 21(2), 1–19. https://doi.org/10.52041/serj.v21i2.37 Çetinkaya-Rundel, M., Hardin, J., Baumer, B. S., McNamara, A., Horton, N. J., & Rundel, C. (2022). An educator’s perspective of the tidyverse. Technology Innovations in Statistics Education, 14(1). https://doi.org/10.5070/T514154352 Çetinkaya-Rundel, M., & Rundel, C. (2018). Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum. The American Statistician, 72(1), 58–65. https://doi.org/10.1080/00031305.2017.1397549 24/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Chamandy, N., Muralidharan, O., & Wager, S. (2015). Teaching Statistics at Google-Scale. The American Statistician, 69(4), 283–291. https://doi.org/10.1080/00031305.2015.1089790 Chance, B., Ben-Zvi, D., Garfield, J., & Medina, E. (2007). The Role of Technology in Improving Student Learning of Statistics. Technology Innovations in Statistics Education, 1(1). https://doi.org/10.5070/T511000026 Chance, B., & Reynolds, S. (2019). Predicting the Kentucky Derby Winner! Sort of. Journal of Statistics Education, 27(2), 120–127. https://doi.org/10.1080/10691898.2019.1623137 Cobb, G. W. (2007). The Introductory Statistics Course: A Ptolemaic Curriculum? Technology Innovations in Statistics Education, 1(1). https://doi.org/10.5070/T511000028 Conti, K. C., & De Carvalho, D. L. (2014). Statistical Literacy: Developing a Youth and Adult Education Statistical Project. Statistics Education Research Journal, 13(2), 164–176. https://doi.org/10.52041/serj.v13i2.288 Curley, B., & Peterson, A. (2022). A Fresh Shot at Statistics in the Classroom: Three Perspectives Using World Cup Soccer Player Data. Journal of Statistics and Data Science Education, 30(1), 86–98. https://doi.org/10.1080/26939169.2021.2008283 Davies, N., & Sheldon, N. (2021). Teaching statistics and data science in England’s schools. Teaching Statistics, 43(S1), S52–S70. https://doi.org/10.1111/test.12276 De Oliveira Souza, L. D., Lopes, C. E., & Fitzallen, N. (2020). Creative Insubordination in Statistics Teaching: Possibilities to Go Beyond Statistical Literacy. Statistics Education Research Journal, 19(1), 73–91. https://doi.org/10.52041/serj.v19i1.120 De Souza Oliveira, F. J., & De Faria Reis, D. A. (2021). The Nepso and Opinion Educative Survey in Latin America: Discussions on Statistical Literacy in the Perspective of This Approach. Statistics Education Research Journal, 20(2), 1–23. https://doi.org/10.52041/serj.v20i2.316 De Veaux, R., Hoerl, R., Snee, R., & Velleman, P. (2022). Toward Holistic Data Science Education. Statistics Education Research Journal, 21(2), 1–12. https://doi.org/10.52041/serj.v21i2.40 Delport, D. H. (2021). Teaching first-year statistics students with COVID-19 real-world data: Graphs. Teaching Statistics, 43(1), 36–43. https://doi.org/10.1111/test.12245 Delport, D. H. (2023). The development of statistical literacy among students: Analyzing messages in media articles with Gal’s worry questions. Teaching Statistics, 45(2), 61–68. https://doi.org/10.1111/test.12308 DePaolo, C. A., Robinson, D. F., & Jacobs, A. (2016). Café Data 2.0: New Data From a New and Improved Café. Journal of Statistics Education, 24(2), 85–103. https://doi.org/10.1080/10691898.2016.1196064 25/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Dogucu, M., & Çetinkaya-Rundel, M. (2021). Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities. Journal of Statistics and Data Science Education, 29(S1), S112–S122. https://doi.org/10.1080/10691898.2020.1787116 Donoghue, T., Voytek, B., & Ellis, S. E. (2021). Teaching Creative and Practical Data Science at Scale. Journal of Statistics and Data Science Education, 29(S1), S27–S39. https://doi.org/10.1080/10691898.2020.1860725 Dunn, P. K. (2013). Comparing the Lifetimes of two Brands of Batteries. Journal of Statistics Education, 21(1), 11. https://doi.org/10.1080/10691898.2013.11889666 Dunn, P. K., Carey, M. D., Farrar, M. B., Richardson, A. M., & McDonald, C. (2017). Introductory Statistics Textbooks and the GAISE Recommendations. The American Statistician, 71(4), 326– 335. https://doi.org/10.1080/00031305.2016.1251972 Dunn, P. K., Donnison, S., Cole, R., & Bulmer, M. (2017). Using a virtual population to authentically teach epidemiology and biostatistics. International Journal of Mathematical Education in Science and Technology, 48(2), 185–201. https://doi.org/10.1080/0020739X.2016.1228015 Dunn, P. K., Richardson, A., Prodromou, T., & Axelsen, T. (2020). Statistics Poster Competitions: An Opportunity To Connect Academics And Teachers. Statistics Education Research Journal, 19(1). https://doi.org/10.52041/serj.v19i1.121 Engel, J. (2017). Statistical Literacy for Active Citizenship: A Call for Data Science Education. Statistics Education Research Journal, 16(1), 44–49. https://doi.org/10.52041/serj.v16i1.213 Engledowl, C. (2019). Heat maps: A case for inclusion in secondary statistics instruction. Teaching Statistics, 41(2), 42–46. https://doi.org/10.1111/test.12177 Engledowl, C., & Weiland, T. (2021). Data (Mis)representation and COVID-19: Leveraging Misleading Data Visualizations For Developing Statistical Literacy Across Grades 6–16. Journal of Statistics and Data Science Education, 29(2), 160–164. https://doi.org/10.1080/26939169.2021.1915215 Erickson, T. (2013). Designing Games for Understanding in a Data Analysis Environment. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013897 Erickson, T., & Chen, E. (2021). Introducing data science with data moves and CODAP. Teaching Statistics, 43, S124–S132. https://doi.org/10.1111/test.12240 Erickson, T., Wilkerson, M., Finzer, W., & Reichsman, F. (2019). Data Moves. Technology Innovations in Statistics Education, 12(1). https://doi.org/10.5070/T5121038001 Estrella, S., Vergara, A., & González, O. (2021). Developing Data Sense: Making Inferences from Variability in Tsunamis at Primary School. Statistics Education Research Journal, 20(2), 1–14. https://doi.org/10.52041/serj.v20i2.413 26/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Evans, C. (2022). Regression, Transformations, and Mixed-Effects with Marine Bryozoans. Journal of Statistics and Data Science Education, 30(2), 198–206. https://doi.org/10.1080/26939169.2022.2074923 Everson, M. G., & Garfield, J. (2008). An Innovative Approach to Teaching Online Statistics Courses. Technology Innovations in Statistics Education, 2(1). https://doi.org/10.5070/T521000031 Fellers, P. S., & Kuiper, S. (2020). Introducing Undergraduates to Concepts of Survey Data Analysis. Journal of Statistics Education, 28(1), 18–24. https://doi.org/10.1080/10691898.2020.1720552 Fergusson, A., & Pfannkuch, M. (2022). Introducing High School Statistics Teachers to Predictive Modelling and APIs Using Code-Driven Tools. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.49 Fergusson, A., & Wild, C. J. (2021). On traversing the data landscape: Introducing APIs to data- science students. Teaching Statistics, 43(S1), S71–S83. https://doi.org/10.1111/test.12266 Ferris, M., & Cheng, S. (2018). Using Twitter to Energize the Introductory Statistics Class. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111032036 Fiksel, J., Jager, L. R., Hardin, J. S., & Taub, M. A. (2019). Using GitHub Classroom to Teach Statistics. Journal of Statistics Education, 27(2), 110–119. https://doi.org/10.1080/10691898.2019.1617089 Finzer, W. (2013). The Data Science Education Dilemma. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013891 Finzer, W., Erickson, T., Swenson, K., & Litwin, M. (2007). On Getting More and Better Data Into the Classroom. Technology Innovations in Statistics Education, 1(1). https://doi.org/10.5070/T511000025 Fleischer, Y., Biehler, R., & Schulte, C. (2022). Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. Statistics Education Research Journal, 21(2), 1–25. https://doi.org/10.52041/serj.v21i2.61 Forbes, S. (2014a). The Coming of Age of Statistics Education in New Zealand, and its Influence Internationally. Journal of Statistics Education, 22(2). https://doi.org/10.1080/10691898.2014.11889699 Forbes, S. (2014b). Using Action Research to Develop a Course in Statistical Inference for Workplace- Based Adults. Journal of Statistics Education, 22(3). https://doi.org/10.1080/10691898.2014.11889711 Forbes, S., Chapman, J., Harraway, J., Stirling, D., & Wild, C. (2014). Use of Data Visualisation in the Teaching of Statistics: A New Zealand Perspective. Statistics Education Research Journal, 13(2), 187–201. https://doi.org/10.52041/serj.v13i2.290 27/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Forbes, S. D. (2012). Data Visualisation: A Motivational and Teaching Tool in Official Statistics. Technology Innovations in Statistics Education, 6(1). https://doi.org/10.5070/T561012851 François, K., Monteiro, C., & Allo, P. (2020). Big-Data Literacy as a New Vocation for Statistical Literacy. Statistics Education Research Journal, 19(1), 194–205. https://doi.org/10.52041/serj.v19i1.130 Freeman, P. E. (2021). Facilitating Authentic Practice for Early Undergraduate Statistics Students. The American Statistician, 75(4), 433–444. https://doi.org/10.1080/00031305.2020.1844293 Frischemeier, D. (2020). Building Statisticians at an Early Age – Statistical Projects Exploring Meaningful Data in Primary School. Statistics Education Research Journal, 19(1), 39–56. https://doi.org/10.52041/serj.v19i1.118 Frischemeier, D., Biehler, R., Podworny, S., & Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data. Teaching Statistics, 43(S1), S182–S189. https://doi.org/10.1111/test.12283 Froelich, A. G., & Nettleton, D. (2013). Does My Baby Really Look Like Me? Using Tests For Resemblance Between Parent and Child to Teach Topics in Categorical Data Analysis. Journal of Statistics Education, 21(2), 3. https://doi.org/10.1080/10691898.2013.11889674 Froelich, A. G., & Stephenson, W. R. (2013). Does Eye Color Depend on Gender? It Might Depend on Who or How You Ask. Journal of Statistics Education, 21(2), 9. https://doi.org/10.1080/10691898.2013.11889680 Frölich, N., & Schellhammer, K. S. (2022). Questionnaire design and sampling procedures for business and economics students: A research-oriented, hands-on course. International Journal of Mathematical Education in Science and Technology, 0(0), 1–19. https://doi.org/10.1080/0020739X.2022.2056722 Fry, K., & Makar, K. (2021). How could we teach data science in primary school? Teaching Statistics, 43(S1), S173–S181. https://doi.org/10.1111/test.12259 Gal, I., & Ograjenšek, I. (2016). Rejoinder: More on Enhancing Statistics Education with Qualitative Ideas. International Statistical Review, 84(2), 202–209. https://doi.org/10.1111/insr.12157 Garcia-Mila, M., Marti, E., Gilabert, S., & Castells, M. (2014). Fifth Through Eighth Grade Students’ Difficulties in Constructing Bar Graphs: Data Organization, Data Aggregation, and Integration of a Second Variable. Mathematical Thinking and Learning, 16(3), 201–233. https://doi.org/10.1080/10986065.2014.921132 Gardner, K. (2013). A data generating review that bops, twists and pulls at misconceptions. Teaching Statistics, 35(1), 8–13. https://doi.org/10.1111/j.1467-9639.2012.00522.x 28/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Gehrke, M., Kistler, T., Lübke, K., Markgraf, N., Krol, B., & Sauer, S. (2021). Statistics education from a data-centric perspective. Teaching Statistics, 43(S1), S201–S215. https://doi.org/10.1111/test.12264 Gerds, T. A. (2016). The Kaplan–Meier theatre. Teaching Statistics, 38(2), 45–49. https://doi.org/10.1111/test.12095 Gibbs, A. L., & Goossens, E. T. (2013). The Evidence for Efficacy of HPV Vaccines: Investigations in Categorical Data Analysis. Journal of Statistics Education, 21(3), 7. https://doi.org/10.1080/10691898.2013.11889688 Gil, E., & Gibbs, A. L. (2017). Promoting Modeling and Covariational Reasoning Among Secondary School Students In The Context Of Big Data. Statistics Education Research Journal, 16(2), 163-. Gómez-Blancarte, A. L., Chávez, R. R., & Chávez Aguilar, R. D. (2021). A Survey of the Teaching of Statistical Literacy, Reasoning and Thinking: Teachers’ Classroom Practice in Mexican High School Education. Statistics Education Research Journal, 20(2), 1–18. https://doi.org/10.52041/serj.v20i2.397 Gomez-Torres, E. (2021). Developing “Recognition of Need For Data” In Secondary School Teachers. Statistics Education Research Journal, 20(2), 1-. https://doi.org/10.52041/serj.v20i2.310 Gonzalez, O. (2021). Teachers’ Conceptions and Professional Knowledge of Variability From Their Interpretation Of Histograms: The Case Of Venezuelan In-Service Secondary Mathematics Teachers. Statistics Education Research Journal, 20(2), 1-. https://doi.org/10.52041/serj.v20i2.412 Gooding, C. L., Lyford, A., & Giaimo, G. N. (2022). Writing goals in U.S. undergraduate data science course outlines: A textual analysis. Teaching Statistics, 44(3), 110–118. https://doi.org/10.1111/test.12314 Gould, R. (2017). Data Literacy Is Statistical Literacy. Statistics Education Research Journal, 16(1), 22– 25. https://doi.org/10.52041/serj.v16i1.209 Gould, R. (2021). Toward data-scientific thinking. Teaching Statistics, 43, S11–S22. https://doi.org/10.1111/test.12267 Gould, R., Bargagliotti, A., & Johnson, T. (2017). An Analysis of Secondary Teachers’ Reasoning With Participatory Sensing Data. Statistics Education Research Journal, 16(2), 305–334. https://doi.org/10.52041/serj.v16i2.194 Grant, R. (2017). Statistical Literacy in the Data Science Workplace. Statistics Education Research Journal, 16(1), 17–21. https://doi.org/10.52041/serj.v16i1.207 Green, J. L., Smith, W. M., Kerby, A. T., Blankenship, E. E., Schmid, K. K., & Carlson, M. A. (2018). Introductory Statistics: Preparing In-Service Middle-Level Mathematics Teachers for 29/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Classroom Research. Statistics Education Research Journal, 17(2), 216–238. https://doi.org/10.52041/serj.v17i2.167 Grimshaw, S. D. (2015). A Framework for Infusing Authentic Data Experiences Within Statistics Courses. The American Statistician, 69(4), 307–314. https://doi.org/10.1080/00031305.2015.1081106 Groth, R. E. (2019). Applying Design-Based Research Findings to Improve the Common Core State Standards for Data and Statistics in Grades 4–6. Journal of Statistics Education, 27(1), 29–36. https://doi.org/10.1080/10691898.2019.1565935 Groth, R. E., & Bergner, J. A. (2013). Mapping the structure of knowledge for teaching nominal categorical data analysis. Educational Studies in Mathematics, 83(2), 247–265. https://doi.org/10.1007/s10649-012-9452-4 Hahs-Vaughn, D. L., Acquaye, H., Griffith, M. D., Jo, H., Matthews, K., & Acharya, P. (2017). Statistical Literacy as a Function of Online Versus Hybrid Course Delivery Format for an Introductory Graduate Statistics Course. Journal of Statistics Education, 25(3), 112–121. https://doi.org/10.1080/10691898.2017.1370363 Haldar, L. C., Wong, N., Heller, J. I., & Konold, C. (2018). Students Making Sense of Multi-level Data. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111031358 Hardin, J. (2018). Dynamic Data in the Statistics Classroom. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111031079 Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Temple Lang, D., & Ward, M. D. (2015). Data Science in Statistics Curricula: Preparing Students to “Think with Data.” The American Statistician, 69(4), 343–353. https://doi.org/10.1080/00031305.2015.1077729 Hardin, J. S., Sarkis, G., & Urc, P. C. (2015). Network Analysis with the Enron Email Corpus. Journal of Statistics Education, 23(2), 2. https://doi.org/10.1080/10691898.2015.11889734 Hassad, R. A. (2013). Faculty Attitude towards Technology-Assisted Instruction for Introductory Statistics in the Context of Educational Reform. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013892 Hassad, R. A. (2020). A Foundation for Inductive Reasoning in Harnessing the Potential of Big Data. Statistics Education Research Journal, 19(1), 238–258. https://doi.org/10.52041/serj.v19i1.133 Helenius, R., D’amelio, A., Campos, P., & Macfeely, S. (2020). Islp Country Coordinators as Ambassadors of Statistical Literacy and Innovations. Statistics Education Research Journal, 19(1), 120–136. https://doi.org/10.52041/serj.v19i1.125 30/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Hicks, S. C., & Irizarry, R. A. (2018). A Guide to Teaching Data Science. The American Statistician, 72(4), 382–391. https://doi.org/10.1080/00031305.2017.1356747 Hobden, S. (2014). When Statistical Literacy Really Matters: Understanding Published Information About the Hiv/Aids Epidemic in South Africa. Statistics Education Research Journal, 13(2), 72–82. https://doi.org/10.52041/serj.v13i2.281 Horton, N. J. (2015). Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward. The American Statistician, 69(2), 138–145. https://doi.org/10.1080/00031305.2015.1032435 Horton, N. J., Alexander, R., Parker, M.,Piekut, A., & Rundel, C. (2022). The Growing Importance of Reproducibility and Responsible Workflow in the Data Science and Statistics Curriculum. Journal of Statistics and Data Science Education, 30(3), 207–208. https://doi.org/10.1080/26939169.2022.2141001 Hourigan, M., & Leavy, A. (2016). What do the stats tell us? Engaging elementary children in probabilistic reasoning based on data analysis. Teaching Statistics, 38(1), 8–15. https://doi.org/10.1111/test.12084 Hourigan, M., & Leavy, A. M. (2020). Using integrated STEMas a stimulus to develop elementary students’ statistical literacy. Teaching Statistics, 42(3), 77–86. https://doi.org/10.1111/test.12229 Hourigan, M., & Leavy, A. M. (2021). Interrogating a measurement conjecture to introduce the concept of statistical association in upper elementary education. Teaching Statistics, 43(2), 62–71. https://doi.org/10.1111/test.12249 Hsu, J. L., Jones, A., Lin, J.-H., & Chen, Y.-R. (2022). Data visualization in introductory business statistics to strengthen students’ practical skills. Teaching Statistics, 44(1), 21–28. https://doi.org/10.1111/test.12291 Hudiburgh, L. M., & Garbinsky, D. (2020). Data Visualization: Bringing Data to Life in an Introductory Statistics Course. Journal of Statistics Education, 28(3), 262–279. https://doi.org/10.1080/10691898.2020.1796399 Isoda, M., Chitmun, S., & Gonzalez, O. (2018). Japanese and Thai Senior High School Mathematics Teachers’ Knowledge of Variability. Statistics Education Research Journal, 17(2), 196–215. https://doi.org/10.52041/serj.v17i2.166 Johnson, R. W. (2017). Discovering patterns in interarrival data. Teaching Statistics, 39(2), 42–46. https://doi.org/10.1111/test.12123 Jones, D. L., & Scariano, S. M. (2014). Measuring the variability of data from other values in the set. Teaching Statistics, 36(3), 93–96. https://doi.org/10.1111/test.12056 31/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Jones, J. D. (2022). Using School Mathematics to Develop Students’ Data Literacy Skills. Mathematics Teacher: Learning and Teaching PK-12, 115(8), 576–581. https://doi.org/10.5951/MTLT.2021.0239 Jones, J. S., & Goldring, J. E. (2017). Telling Stories, Landing Planes and Getting Them Moving—A Holistic Approach to Developing Students’ Statistical Literacy. Statistics Education Research Journal, 16(1), 102–119. https://doi.org/10.52041/serj.v16i1.219 Jones, R. C. (2020). Data Analysis and Critical Thinking Skills Training for Teachers – The Welsh Baccalaureate. Statistics Education Research Journal, 19(1), 92–105. https://doi.org/10.52041/serj.v19i1.123 Kaplan, D. (2007). Computing and Introductory Statistics. Technology Innovations in Statistics Education, 1(1). https://doi.org/10.5070/T511000030 Kaplan, D. (2018). Teaching Stats for Data Science. The American Statistician, 72(1), 89–96. https://doi.org/10.1080/00031305.2017.1398107 Katrina Piatek-Jimenez, Tibor Marcinek, Christine M. Phelps, & Ana Dias. (2012). Helping Students Become Quantitatively Literate. The Mathematics Teacher, 105(9), 692–696. JSTOR. https://doi.org/10.5951/mathteacher.105.9.0692 Khachatryan, D., & Karst, N. (2017). V for Voice: Strategies for Bolstering Communication Skills in Statistics. Journal of Statistics Education, 25(2), 68–78. https://doi.org/10.1080/10691898.2017.1305261 Kim, A. Y., Ismay, C., & Chunn, J. (2018). The fivethirtyeight R Package: “Tame Data” Principles for Introductory Statistics and Data Science Courses. Technology Innovations in Statistics Education, 11(1). https://doi.org/10.5070/T5111035892 Koga, S. (2022). Characteristics of statistical literacy skills from the perspective of critical thinking. Teaching Statistics, 44(2), 59–67. https://doi.org/10.1111/test.12302 Konold, C., Higgins, T., Russell, S. J., & Khalil, K. (2015). Data seen through different lenses. Educational Studies in Mathematics, 88(3), 305–325. https://doi.org/10.1007/s10649-013- 9529-8 Konold, C., & Kazak, S. (2008). Reconnecting Data and Chance. Technology Innovations in Statistics Education, 2(1). https://doi.org/10.5070/T521000032 Koparan, T. (2019). Examination of the dynamic software-supported learning environment in data analysis. International Journal of Mathematical Education in Science and Technology, 50(2), 277–291. https://doi.org/10.1080/0020739X.2018.1494861 Koparan, T., & Güven, B. (2015). The effect of project-based learning on students’ statistical literacy levels for data representation. International Journal of Mathematical Education in Science and Technology, 46(5), 658–686. https://doi.org/10.1080/0020739X.2014.995242 32/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Kozak, M., & Wnuk, A. (2014). Including the Tukey Mean-Difference (Bland–Altman) Plot in a Statistics Course. Teaching Statistics, 36(3), 83–87. https://doi.org/10.1111/test.12032 Kross, S., Peng, R. D., Caffo, B. S., Gooding, I., & Leek, J. T. (2020). The Democratization of Data Science Education. The American Statistician, 74(1), 1–7. https://doi.org/10.1080/00031305.2019.1668849 Kulp, C. W., & Sprechini, G. D. (2016). Teaching the assessment of normality using large easily- generated real data sets. Teaching Statistics, 38(2), 56–62. https://doi.org/10.1111/test.12097 Lasser, J., Manik, D., Silbersdorff, A., Säfken, B., & Kneib, T. (2021). Introductory data science across disciplines, using Python, case studies, and industry consulting projects. Teaching Statistics, 43, S190–S200. https://doi.org/10.1111/test.12243 L’Boy, D., & Nazim Khan, R. (2023). A Rasch-model-based hierarchical framework for statistical literacy and learning. International Journal of Mathematical Education in Science and Technology, 54(9), 1874–1887. https://doi.org/10.1080/0020739X.2023.2261453 Le, D. (2013). Bringing Data to Life into an Introductory Statistics Course with Gapminder. Teaching Statistics, 35(3), 114–122. https://doi.org/10.1111/test.12015 Leavy, A., & Hourigan, M. (2016). Crime scenes and mystery players! Using driving questions to support the development of statistical literacy. Teaching Statistics, 38(1), 29–35. https://doi.org/10.1111/test.12088 Leavy, A., & Hourigan, M. (2018). The role of perceptual similarity, context, and situation when selecting attributes: Considerations made by 5–6-year-olds in data modeling environments. Educational Studies in Mathematics, 97(2), 163–183. https://doi.org/10.1007/s10649-017- 9791-2 Lee, H. S., Mojica, G. F., Thrasher, E. P., & Baumgartner, P. (2022). Investigating Data Like a Data Scientist: Key Practices and Processes. Statistics Education Research Journal, 21(2), 1–23. https://doi.org/10.52041/serj.v21i2.41 Legacy, C., Zieffler, A., Fry, E. B., & Le, L. (2022). Computes: Development Of an Instrument to Measure Introductory Statistics Instructors’ Emphasis On Computational Practices. Statistics Education Research Journal, 21(1), 7–7. https://doi.org/10.52041/serj.v21i1.63 Lem, S., Onghena, P., Verschaffel, L., & Van Dooren, W. (2013). External Representations for Data Distributions: In Search of Cognitive Fit. Statistics Education Research Journal, 12(1), 4–19. https://doi.org/10.52041/serj.v12i1.319 Lesser, L. M., Pearl, D. K., Weber, J. J., Dousa, D. M., Carey, R. P., & Haddad, S. A. (2019). Developing Interactive Educational Songs for Introductory Statistics. Journal of Statistics Education, 27(3). https://www.proquest.com/docview/2351040760/abstract/92048E7684E14A41PQ/1 33/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Li, M., Mickel, A., & Taylor, S. (2018). “Should This Loan be Approved or Denied?”: A Large Dataset with Class Assignment Guidelines. Journal of Statistics Education, 26(1), 55–66. https://doi.org/10.1080/10691898.2018.1434342 Lindsay Reiten & Susanne Strachota. (2016). Promoting Statistical Literacy through Tuva. The Mathematics Teacher, 110(3), 228–231. JSTOR. https://doi.org/10.5951/mathteacher.110.3.0228 Loux, T., & Gibson, A. K. (2019). Using Flint, Michigan, lead data in introductory statistics. Teaching Statistics, 41(3), 85–88. https://doi.org/10.1111/test.12187 Loy, A., Kuiper, S., & Chihara, L. (2019). Supporting Data Science in the Statistics Curriculum. Journal of Statistics Education, 27(1), 2–11. https://doi.org/10.1080/10691898.2018.1564638 Lumbantobing, R., & McFall, T. (2022). An applied statistics teaching lesson that uses NBA playoff data to illustrate uncertainty in sporting contests. Teaching Statistics, 44(3), 104–109. https://doi.org/10.1111/test.12313 Macfeely, S., Campos, P., & Helenius, R. (2017). Key Success Factors for Statistical Literacy Poster Competitions. Statistics Education Research Journal, 16(1), 202–216. https://doi.org/10.52041/serj.v16i1.224 MacKay, J. (2016). Discussion: Acquiring Statistical Literacy and Thinking. International Statistical Review, 84(2), 189–194. https://doi.org/10.1111/insr.12154 MacKay, J. (2022). Data Discovery Challenge Using the COVID-19 Data Portal from New Zealand. Journal of Statistics and Data Science Education, 30(2), 187–190. https://doi.org/10.1080/26939169.2022.2058656 Makar, K. (2014). Young children’s explorations of average through informal inferential reasoning. Educational Studies in Mathematics, 86(1), 61–78. https://doi.org/10.1007/s10649-013- 9526-y Marla A. Sole. (2016). Engaging Students in Survey Design and Data Collection. The Mathematics Teacher, 109(5), 334–340. JSTOR. https://doi.org/10.5951/mathteacher.109.5.0334 Marron, M. M., & Wahed, A. S. (2016). Teaching Missing Data Methodology to Undergraduates Using a Group-Based Project Within a Six-Week Summer Program. Journal of Statistics Education, 24(1), 8–15. https://doi.org/10.1080/10691898.2016.1158018 Maurer, K., & Lock, D. (2016). Comparison of Learning Outcomes for Simulation-based and Traditional Inference Curricula in a Designed Educational Experiment. Technology Innovations in Statistics Education, 9(1). https://doi.org/10.5070/T591026161 McDaniel, S. N., & Green, L. (2012). Independent Interactive Inquiry-Based Learning Modules Using Audio-Visual Instruction in Statistics. Technology Innovations in Statistics Education, 6(1). https://doi.org/10.5070/T561012656 34/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 McGee, M. (2019). Deep Dive into Visual Representation and Interrater Agreement Using Data From a High-School Diving Competition. Journal of Statistics Education, 27(3), 275–287. https://doi.org/10.1080/10691898.2019.1632759 Mike, K., & Hazzan, O. (2022). Machine Learning for Non-Major Data Science Students: A White Box Approach. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.45 Molnar, A. (2013). Discussion: What do Instructors of Statistics Need to Know About Technology, and How Can They Best Be Taught? Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013896 Myint, L., Hadavand, A., Jager, L., & Leek, J. (2020). Comparison of Beginning R Students’ Perceptions of Peer-Made Plots Created in Two Plotting Systems: A Randomized Experiment. Journal of Statistics Education, 28(1), 98–108. https://doi.org/10.1080/10691898.2019.1695554 Nazzaro, V., Rose, J., & Dierker, L. (2020). A Comparison of Future Course Enrollment Among Students Completing One Of Four Different Introductory Statistics Courses. Statistics Education Research Journal, 19(3), 6–17. Neumann, D. L., Hood, M., & Neumann, M. M. (2013). Using Real-Life Data When Teaching Statistics: Student Perceptions of This Strategy in an Introductory Statistics Course. Statistics Education Research Journal, 12(2), 59–70. https://doi.org/10.52041/serj.v12i2.304 Nicholson, J., Ridgway, J., & McCusker, S. (2013). Getting Real Statistics into all Curriculum Subject Areas: Can Technology Make this a Reality? Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013906 Nilsson, P. (2013). Challenges in Seeing Data as Useful Evidence in Making Predictions on the Probability of a Real-World Phenomenon. Statistics Education Research Journal, 12(2), 71– 83. https://doi.org/10.52041/serj.v12i2.305 Nolan, D., & Perrett, J. (2016). Teaching and Learning Data Visualization: Ideas and Assignments. The American Statistician, 70(3), 260–269. https://doi.org/10.1080/00031305.2015.1123651 Nolan, D., & Temple Lang, D. (2015). Explorations in Statistics Research: An Approach to Expose Undergraduates to Authentic Data Analysis. The American Statistician, 69(4), 292–299. https://doi.org/10.1080/00031305.2015.1073624 Noll, J., & Kirin, D. (2016). Student Approaches to Constructing Statistical Models using TinkerPlots TM. Technology Innovations in Statistics Education, 9(1). https://doi.org/10.5070/T591023693 Noll, J., & Tackett, M. (2023). Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethics. Teaching Statistics, 45(S1), S5–S21. https://doi.org/10.1111/test.12345 35/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Nowacki, A. S. (2013). Data Sharing and the Development of the Cleveland Clinic Statistical Education Dataset Repository. Journal of Statistics Education, 21(1), 4. https://doi.org/10.1080/10691898.2013.11889660 Nowacki, A. S. (2015). Teaching Statistics from the Operating Table: Minimally Invasive and Maximally Educational. Journal of Statistics Education, 23(1), 6. https://doi.org/10.1080/10691898.2015.11889726 Odom, A. L., & Bell, C. V. (2017). Developing PK-12 Preservice Teachers’ Skills for Understanding Data-Driven Instruction Through Inquiry Learning. Journal of Statistics Education, 25(1), 29– 37. https://doi.org/10.1080/10691898.2017.1288557 Oslington, G., Mulligan, J., & Van Bergen, P. (2020). Third-graders’ predictive reasoning strategies. Educational Studies in Mathematics, 104(1), 5–24. https://doi.org/10.1007/s10649-020- 09949-0 Ostblom, J., & Timbers, T. (2022). Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice. Journal of Statistics and Data Science Education, 30(3), 241– 250. https://doi.org/10.1080/26939169.2022.2074922 Peng, R. D., Chen, A., Bridgeford, E., Leek, J. T., & Hicks, S. C. (2021). Diagnosing Data Analytic Problems in the Classroom. Journal of Statistics and Data Science Education, 29(3), 267–276. https://doi.org/10.1080/26939169.2021.1971586 Peterson, A. D., & Ziegler, L. (2021). Building a Multiple Linear Regression Model with LEGO Brick Data. Journal of Statistics and Data Science Education, 29(3), 297–303. https://doi.org/10.1080/26939169.2021.1946450 Phelps, A. L., & Szabat, K. A. (2017). The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What? The American Statistician, 71(2), 155–161. https://doi.org/10.1080/00031305.2016.1277160 Piatek-Jimenez, K., Marcinek, T., Phelps, C. M., & Dias, A. (2012). Helping Students Become Quantitatively Literate. The Mathematics Teacher, 105(9), 692–696. https://doi.org/10.5951/mathteacher.105.9.0692 Podworny, S., Husing, S., & Schulte, C. (2022). A Place for A Data Science Introduction in School: Between Statistics and Programming. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.46 Poling, L., & Weiland, T. (2020). Using an interactive platform to recognize the intersection of social and spatial inequalities. Teaching Statistics, 42(3), 108–116. https://doi.org/10.1111/test.12234 Prodromou, T., & Dunne, T. (2017). Statistical Literacy in Data Revolution Era: Building Blocks and Instructional Dilemmas. Statistics Education Research Journal, 16(1), 38–43. https://doi.org/10.52041/serj.v16i1.212 36/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Queiroz, T., Monteiro, C., Carvalho, L., & François, K. (2017). Interpretation of Statistical Data: The Importance of Affective Expressions. Statistics Education Research Journal, 16(1), 163–180. https://doi.org/10.52041/serj.v16i1.222 Rao, V. N. V., Legacy, C., Zieffler, A., & delMas, R. (2023). Designing a sequence of activities to build reasoning about data and visualization. Teaching Statistics, 45(S1), S80–S92. https://doi.org/10.1111/test.12341 Reinhart, A., & Genovese, C. R. (2021). Expanding the Scope of Statistical Computing: Training Statisticians to Be Software Engineers. Journal of Statistics and Data Science Education, 29(S1), S7–S15. https://doi.org/10.1080/10691898.2020.1845109 Reiten, L., & Strachota, S. (2016). Promoting Statistical Literacy through Tuva. The Mathematics Teacher, 110(3), 228–231. https://doi.org/10.5951/mathteacher.110.3.0228 Reston, E. (2013). An Outcome-Based Framework for Technology Integration in Higher Education Statistics Curricula For Non-Majors. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013894 Richardson, A. M., & Dunn, P. K. (2021). Simple interventions to assist students to engage with the language of data science and statistics. Teaching Statistics, 43(S1), S148–S156. https://doi.org/10.1111/test.12247 Ridgway, J. (2016). Implications of the Data Revolution for Statistics Education. International Statistical Review, 84(3), 528–549. https://doi.org/10.1111/insr.12110 Ridgway, J. (2021). Covid and data science: Understanding R0 could change your life. Teaching Statistics, 43(S1), S84–S92. https://doi.org/10.1111/test.12273 Ridgway, J., Nicholson, J., & McCusker, S. (2013). “Open Data” and the Semantic Web Require a Rethink on Statistics Teaching. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013907 Rivera, R., Marazzi, M., & Torres-Saavedra, P. A. (2019). Incorporating Open Data into Introductory Courses in Statistics. Journal of Statistics Education, 27(3), 198–207. https://doi.org/10.1080/10691898.2019.1669506 Roepke, T. L., & Gallagher, D. K. (2015). Using Literacy Strategies to Teach Precalculus and Calculus. The Mathematics Teacher, 108(9), 672–678. https://doi.org/10.5951/mathteacher.108.9.0672 Rossman, A. J., Laurent, R. St., & Tabor, J. (2015). Advanced Placement Statistics: Expanding the Scope of Statistics Education. The American Statistician, 69(2), 121–126. https://doi.org/10.1080/00031305.2015.1033985 37/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Roth, W.-M., & Temple, S. (2014). On understanding variability in data: A study of graph interpretation in an advanced experimental biology laboratory. Educational Studies in Mathematics, 86(3), 359–376. https://doi.org/10.1007/s10649-014-9535-5 Rubel, L. H., Nicol, C., & Chronaki, A. (2021). A critical mathematics perspective on reading data visualizations: Reimagining through reformatting, reframing, and renarrating. Educational Studies in Mathematics, 108(1–2), 249–268. https://doi.org/10.1007/s10649-021-10087-4 Rubin, A. (2021). What to consider when we consider data. Teaching Statistics, 43, S23–S33. https://doi.org/10.1111/test.12275 Sabbag, A., Garfield, J., & Zieffler, A. (2018). Assessing Statistical Literacy And Statistical Reasoning: The Reali Instrument. Statistics Education Research Journal, 17(2), 141–160. https://doi.org/10.52041/serj.v17i2.163 Salcedo, A. (2014). Statistics Test Questions: Content and Trends. Statistics Education Research Journal, 13(2), 202–217. https://doi.org/10.52041/serj.v13i2.291 Schield, M. (2017). Gaise 2016 Promotes Statistical Literacy. Statistics Education Research Journal, 16(1), 50–54. https://doi.org/10.52041/serj.v16i1.214 Schwab-McCoy, A., Baker, C. M., & Gasper, R. E. (2021). Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years. Journal of Statistics and Data Science Education, 29(S1), S40–S50. https://doi.org/10.1080/10691898.2020.1851159 Shaltayev, D. S., Hodges, H., & Hasbrouck, R. B. (2010). VISA: Reducing Technological Impact on Student Learning in an Introductory Statistics Course. Technology Innovations in Statistics Education, 4(1). https://doi.org/10.5070/T541000041 Sole, M. A. (2016a). Engaging Students in Survey Design and Data Collection. The Mathematics Teacher, 109(5), 334–340. https://doi.org/10.5951/mathteacher.109.5.0334 Sole, M. A. (2016b). Statistical Literacy: Data Tell a Story. The Mathematics Teacher, 110(1), 26–32. https://doi.org/10.5951/mathteacher.110.1.0026 Sole, M. A., & Weinberg, S. L. (2017). What’s Brewing? A Statistics Education Discovery Project. Journal of Statistics Education, 25(3), 137–144. https://doi.org/10.1080/10691898.2017.1395302 Soledad Fernández, M., Pomilio, C., Cueto, G., Filloy, J., Gonzalez-Arzac, A., Lois-Milevicich, J., & Pérez, A. (2020). Improving Skills to Teach Statistics in Secondary School Through Activity- Based Workshops. Statistics Education Research Journal, 19(1), 106–122. https://doi.org/10.52041/serj.v19i1.124 Sønvisen, S. A. (2023). Motivation for learning statistics: An example from fishery and aquaculture science. Teaching Statistics, 45(2), 85–99. https://doi.org/10.1111/test.12334 38/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Stander, J., & Dalla Valle, L. (2017). On Enthusing Students About Big Data and Social Media Visualization and Analysis Using R, RStudio, and RMarkdown. Journal of Statistics Education, 25(2), 60–67. https://doi.org/10.1080/10691898.2017.1322474 Stern, D. (2013). Developing Statistics Education in Kenya Through Technological Innovations at all Academic Levels. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013905 Stern, D., Stern, R., Parsons, D., Musyoka, J., Torgbor, F., & Mbasu, Z. (2020). Envisioning Change in The Statistics-Education Climate. Statistics Education Research Journal, 19(1), 206–225. https://doi.org/10.52041/serj.v19i1.131 Stoudt, S. (2022). Collaborative Writing Workflows in the Data-Driven Classroom: A Conversation Starter. Journal of Statistics and Data Science Education, 30(3), 282–288. https://doi.org/10.1080/26939169.2022.2082602 Stoudt, S., Scotina, A. D., & Luebke, K. (2022). Supporting Statistics and Data Science Education with learnr. Technology Innovations in Statistics Education, 14(1). https://doi.org/10.5070/T514156264 Strachota, S., & Reiten, L. (2017). Fostering Productive Statistical Skepticism. The Mathematics Teacher, 111(3), 222–224. https://doi.org/10.5951/mathteacher.111.3.0222 Stratton, C., Green, J. L., & Hoegh, A. (2021). Not just normal: Exploring power with Shiny apps. Technology Innovations in Statistics Education, 13(1). https://doi.org/10.5070/T513146468 Strayer, J. F., & Edwards, M. T. (2015). Smarter Cookies. The Mathematics Teacher, 108(8), 608–615. https://doi.org/10.5951/mathteacher.108.8.0608 Stump, S. L., Bryan, J. A., & McConnell, T. J. (2016). Making STEM Connections. The Mathematics Teacher, 109(8), 576–583. https://doi.org/10.5951/mathteacher.109.8.0576 Sutherland, S., & Ridgway, J. (2017). Interactive Visualisations and Statistical Literacy. Statistics Education Research Journal, 16(1), 26–30. https://doi.org/10.52041/serj.v16i1.210 Tan, K. S., Elkin, E. B., & Satagopan, J. M. (2022). A Model for an Undergraduate Research Experience Program in Quantitative Sciences. Journal of Statistics and Data Science Education, 30(1), 65–74. https://doi.org/10.1080/26939169.2021.2016036 Tena L. Roepke & Debra K. Gallagher. (2015). Using Literacy Strategies to Teach Precalculus and Calculus. The Mathematics Teacher, 108(9), 672–678. JSTOR. https://doi.org/10.5951/mathteacher.108.9.0672 Theobold, A., & Hancock, S. (2019). How Environmental Science Graduate Students Acquire Statistical Computing Skills. Statistics Education Research Journal, 18(2), 68–85. https://doi.org/10.52041/serj.v18i2.141 39/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Theobold, A. S., Hancock, S. A., & Mannheimer, S. (2021). Designing Data Science Workshops for Data-Intensive Environmental Science Research. Journal of Statistics and Data Science Education, 29(S1), S83–S94. https://doi.org/10.1080/10691898.2020.1854636 Thompson, J., & Irgens, G. A. (2022). Data Detectives: A Data Science Program for Middle Grade Learners. Journal of Statistics and Data Science Education, 30(1), 29–38. https://doi.org/10.1080/26939169.2022.2034489 Towse, J., Davies, R., Ball, E., James, R., Gooding, B., & Ivory, M. (2022). LUSTRE: An Online Data Management and Student Project Resource. Journal of Statistics and Data Science Education, 30(3), 266–273. https://doi.org/10.1080/26939169.2022.2118645 Trafimow, D. (2016). The attenuation of correlation coefficients: A statistical literacy issue. Teaching Statistics, 38(1), 25–28. https://doi.org/10.1111/test.12087 Tunstall, S. L. (2018). Investigating College Students’ Reasoning with Messages of Risk and Causation. Journal of Statistics Education, 26(2), 76–86. https://doi.org/10.1080/10691898.2018.1456989 Ubilla, F. M., & Gorgorió, N. (2021). From a source of real data to a brief news report: Introducing first-year preservice teachers to the basic cycle of learning from data. Teaching Statistics, 43(S1), S110–S123. https://doi.org/10.1111/test.12246 Utts, J. (2021). Enhancing Data Science Ethics Through Statistical Education and Practice. International Statistical Review, 89(1), 1–17. https://doi.org/10.1111/insr.12446 Vance, E. A. (2021). Using Team-Based Learning to Teach Data Science. Journal of Statistics and Data Science Education, 29(3), 277–296. https://doi.org/10.1080/26939169.2021.1971587 Vance, E. A., Glimp, D. R., Pieplow, N. D., Garrity, J. M., & Melbourne, B. A. (2022). Integrating the Humanities into Data Science Education: Reimagining the Introductory Data Science Course. Statistics Education Research Journal, 21(2), 1–18. https://doi.org/10.52041/serj.v21i2.42 Vance, E. A., & Smith, H. S. (2019). The ASCCR Frame for Learning Essential Collaboration Skills. Journal of Statistics Education, 27(3), 265–274. https://doi.org/10.1080/10691898.2019.1687370 Wagaman, J. C. (2017). Introductory statistics in the garden. Teaching Statistics, 39(2), 52–56. https://doi.org/10.1111/test.12125 Wang, X., Reich, N. G., & Horton, N. J. (2019). Enriching Students’ Conceptual Understanding of Confidence Intervals: An Interactive Trivia-Based Classroom Activity. The American Statistician, 73(1), 50–55. https://doi.org/10.1080/00031305.2017.1305294 Wang, X., Rush, C., & Horton, N. J. (2017). Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often. Technology Innovations in Statistics Education, 10(1). https://doi.org/10.48550/arXiv.1705.08544 40/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Watson, J., & Donne, J. (2009). TinkerPlots as a Research Tool to Explore Student Understanding. Technology Innovations in Statistics Education, 3(1). https://doi.org/10.5070/T531000034 Watson, J., & English, L. (2017). Reaction Time in Grade 5: Data Collection Within the Practice of Statistics. Statistics Education Research Journal, 16(1), 262–293. https://doi.org/10.52041/serj.v16i1.231 Watson, J., Fitzallen, N., English, L., & Wright, S. (2020). Introducing statistical variation in Year 3 in a STEM context: Manufacturing licorice. International Journal of Mathematical Education in Science and Technology, 51(3), 354–387. https://doi.org/10.1080/0020739X.2018.1562117 Watson, J., Fitzallen, N., Wright, S., & Kelly, B. (2022). Characterizing Student Experience of Variation Within a Stem Context: Improving Catapults. Statistics Education Research Journal, 21(1), 9– 9. https://doi.org/10.52041/serj.v21i1.7 Weiland, T. (2017). Problematizing statistical literacy: An intersection of critical and statistical literacies. Educational Studies in Mathematics, 96(1), 33–47. https://doi.org/10.1007/s10649-017-9764-5 Weiland, T, & Sundrani, A. (2022). Opportunities for K-8 Students to Learn Statistics Created by States’ Standards in the United States. Journal of Statistics and Data Science Education, 30(2), 165–178. https://doi.org/10.1080/26939169.2022.2075814 Wild, C. J. (2017). Statistical Literacy as the Earth Moves. Statistics Education Research Journal, 16(1), 31–37. https://doi.org/10.52041/serj.v16i1.211 Wilkerson, M. H., Lanouette, K., & Shareff, R. L. (2022). Exploring variability during data preparation: A way to connect data, chance, and context when working with complex public datasets. Mathematical Thinking and Learning, 24(4), 312–330. https://doi.org/10.1080/10986065.2021.1922838 Wilson, M., Ross, A., & Casey, S. (2021). A classroom-ready activity on educational disparities in the United States. Teaching Statistics, 43(S1), S93–S97. https://doi.org/10.1111/test.12252 Witt, G. (2013). Using Data from Climate Science to Teach Introductory Statistics. Journal of Statistics Education, 21(1), 12. https://doi.org/10.1080/10691898.2013.11889667 Yan, D., & Davis, G. E. (2019). A First Course in Data Science. Journal of Statistics Education, 27(2), 99–109. https://doi.org/10.1080/10691898.2019.1623136 Yolcu, A. (2014). Middle School Students’ Statistical Literacy: Role of Grade Level and Gender. Statistics Education Research Journal, 13(2), 118–131. https://doi.org/10.52041/serj.v13i2.285 Zakari, I. S. (2020). Promoting Statistics in The Era Of Data Science And Data-Driven Innovations. Statistics Education Research Journal, 19(1), 226–237. https://doi.org/10.52041/serj.v19i1.132 41/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Zapata-Cardona, L. (2023). The possibilities of exploring nontraditional datasets with young children. Teaching Statistics, 45(S1), S22–S29. https://doi.org/10.1111/test.12349 Zheng, Q., & Lu, Y. (2016). Do you catch undersized fish? Let’s Go fishing to learn some important concepts in multiple testing. Teaching Statistics, 38(3), 91–97. https://doi.org/10.1111/test.12107 Zhu, Y., Hernandez, L. M., Mueller, P., Dong, Y., & Forman, M. R. (2013). Data Acquisition and Preprocessing in Studies on Humans: What is Not Taught in Statistics Classes? The American Statistician, 67(4), 235–241. https://doi.org/10.1080/00031305.2013.842498 Ziegler, L., & Garfield, J. (2013). Exploring students’ intuitive ideas of randomness using an iPod shuffle activity. Teaching Statistics, 35(1), 2–7. https://doi.org/10.1111/j.1467- 9639.2012.00531.x Ziegler, L., & Garfield, J. (2018). Developing A Statistical Literacy Assessment For The Modern Introductory Statistics Course. Statistics Education Research Journal, 17(2), 161–178. https://doi.org/10.52041/serj.v17i2.164 Appendix C: Removed Articles Aberson, C. (2021). Building Interactive Tutorials for Teaching Psychological Statistics Online with learnr. Technology Innovations in Statistics Education, 13(1). https://doi.org/10.5070/T513153822 Alexis Stevens & John Stevens. (2016). Using Mathematics to Elect the U.S. President. The Mathematics Teacher, 110(3), 192–198. JSTOR. https://doi.org/10.5951/mathteacher.110.3.0192 Austin, P. C. (2017). A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications. International Statistical Review, 85(2), 185–203. https://doi.org/10.1111/insr.12214 Ayalon, M., Watson, A., & Lerman, S. (2015). Functions represented as linear sequential data: Relationships between presentation and student responses. Educational Studies in Mathematics, 90(3), 321–339. https://doi.org/10.1007/s10649-015-9628-9 Baffour, B., Chandra, H., & Martinez, A. (2019). Localised Estimates of Dynamics of Multi- dimensional Disadvantage: An Application of the Small Area Estimation Technique Using Australian Survey and Census Data. International Statistical Review, 87(1), 1– 23. https://doi.org/10.1111/insr.12270 Beemer, J., Spoon, K., Fan, J., Stronach, J., Frazee, J. P., Bohonak, A. J., & Levine, R. A. (2018). Assessing Instructional Modalities: Individualized Treatment Effects for Personalized Learning. Journal of Statistics Education, 26(1), 31–39. https://doi.org/10.1080/10691898.2018.1426400 42/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Bulmer, M., & Haladyn, J. K. (2011). Life on an Island: A Simulated Population to Support Student Projects in Statistics. Technology Innovations in Statistics Education, 5(1). https://doi.org/10.5070/T551000187 Carey, M. D., & Dunn, P. K. (2018). Facilitating Language-Focused Cooperative Learning In Introductory Statistics Classrooms: A Case Study. Statistics Education Research Journal, 17(2), 30–50. https://doi.org/10.52041/serj.v17i2.157 Çetinkaya-Rundel, M., & Rundel, C. (2018). Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum. The American Statistician, 72(1), 58–65. https://doi.org/10.1080/00031305.2017.1397549 Chad Leith, Elena Rose, & Tony King. (2016). Teaching Mathematics and Language to English Learners. The Mathematics Teacher, 109(9), 670–678. JSTOR. https://doi.org/10.5951/mathteacher.109.9.0670 Cronin, A., Intepe, G., Shearman, D., & Sneyd, A. (2019). Analysis using natural language processing of feedback data from two mathematics support centres. International Journal of Mathematical Education in Science and Technology, 50(7), 1087–1103. https://doi.org/10.1080/0020739X.2019.1656831 De Oliveira, V. (2020). Models for Geostatistical Binary Data: Properties and Connections. The American Statistician, 74(1), 72–79. https://doi.org/10.1080/00031305.2018.1444674 Doi, J., Potter, G., Wong, J., Alcaraz, I., & Chi, P. (2016). Web Application Teaching Tools for Statistics Using R and Shiny. Technology Innovations in Statistics Education, 9(1). https://doi.org/10.5070/T591027492 Dunn, P. K. (2022). The impact of using artificial data in undergraduate statistics students’ projects due to COVID-19 lockdowns. International Journal of Mathematical Education in Science and Technology, ahead-of-print(ahead-of-print), 1–10. https://doi.org/10.1080/0020739X.2022.2056095 Dunn, P. K., Marshman, M., McDougall, R., & Wiegand, A. (2015). Teachers and Textbooks: On Statistical Definitions in Senior Secondary Mathematics. Journal of Statistics Education, 23(3). https://doi.org/10.1080/10691898.2015.11889744 Emily P. Thrasher & Ayanna D. Perry. (2015). High-Leverage Apps for the Mathematics Classroom: WolframAlpha. The Mathematics Teacher, 109(1), 66–70. JSTOR. https://doi.org/10.5951/mathteacher.109.1.0066 English, L. D., & Watson, J. M. (2016). Development of Probabilistic Understanding in Fourth Grade. Journal for Research in Mathematics Education, 47(1), 28–62. https://doi.org/10.5951/jresematheduc.47.1.0028 43/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Fergusson, A., & Pfannkuch, M. (2022). Introducing teachers who use GUI-driven tools for the randomization test to code-driven tools. Mathematical Thinking and Learning, 24(4), 336–356. https://doi.org/10.1080/10986065.2021.1922856 Gabrosek, J., & O’Kelly, L. (2018). R-E-S-P-E-C-T: The Role of Race, Gender, and Radio Consultants on Radio Airplay in 1960s Chicago, IL and Grand Rapids, MI. Journal of Statistics Education, 26(3), 223–233. https://doi.org/10.1080/10691898.2018.1506953 Gal, I., & Geiger, V. (2022). Welcome to the era of vague news: A study of the demands of statistical and mathematical products in the COVID-19 pandemic media. Educational Studies in Mathematics, 111(1), 5–28. https://doi.org/10.1007/s10649-022-10151-7 Gundlach, E., Richards, K. A. R., Nelson, D., & Levesque-Bristol, C. (2015). A Comparison of Student Attitudes, Statistical Reasoning, Performance, and Perceptions for Web-Augmented Traditional, Fully Online, and Flipped Sections of a Statistical Literacy Class. Journal of Statistics Education, 23(1), 3. https://doi.org/10.1080/10691898.2015.11889723 Harraway, J. A. (2012). Learning Statistics Using Motivational Videos, Real Data and Free Software. Technology Innovations in Statistics Education, 6(1). https://doi.org/10.5070/T561000186 Heinzman, E. (2022). “I Love Math Only If It’s Coding”: A Case Study of Student Experiences In An Introduction To Data Science Course. Statistics Education Research Journal, 21(2), 1-. https://doi.org/10.52041/serj.v21i2.43 Hermans, L., Molenberghs, G., Aerts, M., Kenward, M. G., & Verbeke, G. (2018). A Tutorial on the Practical Use and Implication of Complete Sufficient Statistics. International Statistical Review, 86(3), 403–414. https://doi.org/10.1111/insr.12261 Horton, N. J., & Hardin, J. S. (2021). Integrating Computing in the Statistics and Data Science Curriculum: Creative Structures, Novel Skills and Habits, and Ways to Teach Computational Thinking. Journal of Statistics and Data Science Education, 29(S1), S1–S3. https://doi.org/10.1080/10691898.2020.1870416 Humenberger, H. (2020). How does the change of a single data point affect the variance, and why? Teaching Statistics, 42(3), 87–90. https://doi.org/10.1111/test.12230 Jankvist, U. T., & Niss, M. (2020). Upper secondary school students’ difficulties with mathematical modelling. International Journal of Mathematical Education in Science and Technology, 51(4), 467–496. https://doi.org/10.1080/0020739X.2019.1587530 Jeremy Strayer & Amber Matuszewski. (2016). Statistical Literacy: Simulations with Dolphins. The Mathematics Teacher, 109(8), 606–611. JSTOR. https://doi.org/10.5951/mathteacher.109.8.0606 Johnson, R. W., Kliche, D. V., & Smith, P. L. (2015). Modeling Raindrop Size. Journal of Statistics Education, 23(1), 5. https://doi.org/10.1080/10691898.2015.11889725 44/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Kathleen H. Offenholley. (2013). Bundled-Up Babies and Dangerous Ice Cream: Correlation Puzzlers. The Mathematics Teacher, 106(6), 418–422. JSTOR. https://doi.org/10.5951/mathteacher.106.6.0418 Kuiper, S., & Sturdivant, R. X. (2015). Using Online Game-Based Simulations to Strengthen Students’ Understanding of Practical Statistical Issues in Real-World Data Analysis. The American Statistician, 69(4), 354–361. https://doi.org/10.1080/00031305.2015.1075421 Laurie H. Rubel, Michael Driskill, & Lawrence M. Lesser. (2012). Decennial Redistricting: Rich Mathematics in Context. The Mathematics Teacher, 106(3), 206–211. JSTOR. https://doi.org/10.5951/mathteacher.106.3.0206 Lesser, L. M., & Santos, M. (2023). A Survey on How College Students in a Statistical Literacy Course Apply Statistics Terms to People. Journal of Statistics and Data Science Education, 0(0), 1–15. https://doi.org/10.1080/26939169.2023.2193307 Lindsay M. Keazer & Rahul S. Menon. (2016). Reasoning and Sense Making Begins with the Teacher. The Mathematics Teacher, 109(5), 342–349. JSTOR. https://doi.org/10.5951/mathteacher.109.5.0342 Lu, Y., & Henning, K. S. S. (2013). Are statisticians cold-blooded bosses? A new perspective on the ‘old’ concept of statistical population. Teaching Statistics, 35(1), 66–71. https://doi.org/10.1111/j.1467-9639.2012.00524.x Lübke, K., Gehrke, M., Horst, J., & Szepannek, G. (2020). Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data. Journal of Statistics Education, 28(2), 133–139. https://doi.org/10.1080/10691898.2020.1752859 MacGillivray, H. (2021). Statistics and data science must speak together. Teaching Statistics, 43(S1), S5–S10. https://doi.org/10.1111/test.12281 Manage, A. B. W., & Scariano, S. M. (2013). An Introductory Application of Principal Components to Cricket Data. Journal of Statistics Education, 21(3), 8. https://doi.org/10.1080/10691898.2013.11889689 Marla A. Sole. (2017). Financial Education: Increase Your Purchasing Power. The Mathematics Teacher, 111(1), 60–64. JSTOR. https://doi.org/10.5951/mathteacher.111.1.0060 Marwick, B., Boettiger, C., & Mullen, L. (2018). Packaging Data Analytical Work Reproducibly Using R (and Friends). The American Statistician, 72(1), 80–88. https://doi.org/10.1080/00031305.2017.1375986 McCune, D., & Tunstall, S. L. (2019). Calculated democracy—E xplorations in gerrymandering. Teaching Statistics, 41(2), 47–53. https://doi.org/10.1111/test.12181 45/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 McDaniel, S. N., & Green, L. B. (2012). Using Applets and Video Instruction to Foster Students’ Understanding of Sampling Variability. Technology Innovations in Statistics Education, 6(1). https://doi.org/10.5070/T561000177 McGowan, H. M., & Gunderson, B. K. (2010). A Randomized Experiment Exploring How Certain Features of Clicker Use Effect Undergraduate Students’ Engagement and Learning in Statistics. Technology Innovations in Statistics Education, 4(1). https://doi.org/10.5070/T541000042 Mclaughlin, J. E., & Kang, I. (2017). A Flipped Classroom Model for a Biostatistics Short Course. Statistics Education Research Journal, 16(2), 441–453. https://doi.org/10.52041/serj.v16i2.200 Michael J. Caulfield. (2012). What If? How Apportionment Methods Choose Our Presidents. The Mathematics Teacher, 106(3), 178–183. JSTOR. https://doi.org/10.5951/mathteacher.106.3.0178 Mocko, M. (2013). Selecting Technology to Promote Learning in an Online Introductory Statistics Course. Technology Innovations in Statistics Education, 7(2). https://doi.org/10.5070/T572013893 Murawska, J. M., & Nabb, K. A. (2015). Corvettes, Curve Fitting, and Calculus. The Mathematics Teacher, 109(2), 128–135. https://doi.org/10.5951/mathteacher.109.2.0128 Newfeld, D. (2016). A first assignment to create student buy-in in an introductory business statistics course. Teaching Statistics, 38(3), 87–90. https://doi.org/10.1111/test.12106 Nirmala Naresh, Suzanne R. Harper, Jane M. Keiser, & Norm Krumpe. (2014). Probability Explorations in a Multicultural Context. The Mathematics Teacher, 108(3), 184–192. JSTOR. https://doi.org/10.5951/mathteacher.108.3.0184 North, D., Gal, I., & Zewotir, T. (2014). Building Capacity for Developing Statistical Literacy in a Developing Country: Lessons Learned from an Intervention. Statistics Education Research Journal, 13(2), 15–27. https://doi.org/10.52041/serj.v13i2.276 Planas, N. (2014). One speaker, two languages: Learning opportunities in the mathematics classroom. Educational Studies in Mathematics, 87(1), 51–66. https://doi.org/10.1007/s10649-014-9553-3 Porcu, E., Alegria, A., & Furrer, R. (2018). Modeling Temporally Evolving and Spatially Globally Dependent Data. International Statistical Review, 86(2), 344–377. https://doi.org/10.1111/insr.12266 Rethlefsen, M. L., Norton, H. F., Meyer, S. L., MacWilkinson, K. A., Smith II, P. L., & Ye, H. (2022). Interdisciplinary Approaches and Strategies from Research Reproducibility 2020: Educating for Reproducibility. Journal of Statistics and Data Science Education, 30(3), 219–227. https://doi.org/10.1080/26939169.2022.2104767 46/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Rubel, L. H., & Nicol, C. (2020). The power of place: Spatializing critical mathematics education. Mathematical Thinking and Learning, 22(3), 173–194. https://doi.org/10.1080/10986065.2020.1709938 Rubin, A. (2007). Much Has Changed; Little Has Changed: Revisiting the Role of Technology in Statistics Education 1992-2007. Technology Innovations in Statistics Education, 1(1). https://doi.org/10.5070/T511000027 Schindler, M., & Lilienthal, A. J. (2019). Domain-specific interpretation of eye tracking data: Towards a refined use of the eye-mind hypothesis for the field of geometry. Educational Studies in Mathematics, 101(1), 123–139. https://doi.org/10.1007/s10649-019- 9878-z Sun, D. L., & Alfredo, J. (2019). A Modern Look at Freedman’s Box Model. Technology Innovations in Statistics Education, 12(1). https://doi.org/10.5070/T5121044395 Utts, J. (2015). The Many Facets of Statistics Education: 175 Years of Common Themes. The American Statistician, 69(2), 100–107. https://doi.org/10.1080/00031305.2015.1033981 Victor Mateas. (2013). Connecting Algebra to Economics. The Mathematics Teacher, 107(4), 298– 304. JSTOR. https://doi.org/10.5951/mathteacher.107.4.0298 Wagaman, A. (2016). Meeting Student Needs for Multivariate Data Analysis: A Case Study in Teaching an Undergraduate Multivariate Data Analysis Course. The American Statistician, 70(4), 405–412. https://doi.org/10.1080/00031305.2016.1201005 Weiland, T. (2019). The Contextualized Situations Constructed for the Use of Statistics by School Mathematics Textbooks. Statistics Education Research Journal, 18(2), 18–38. https://doi.org/10.52041/serj.v18i2.138 Zhou, J., Zhang, Z., Li, Z., & Zhang, J. (2015). Coarsened Propensity Scores and Hybrid Estimators for Missing Data and Causal Inference. International Statistical Review, 83(3), 449–471. https://doi.org/10.1111/insr.12082 Ziemer, K. S., Pires, B., Lancaster, V., Keller, S., Orr, M., & Shipp, S. (2018). A New Lens on High School Dropout: Use of Correspondence Analysis and the Statewide Longitudinal Data System. The American Statistician, 72(2), 191–198. https://doi.org/10.1080/00031305.2017.1322002 47/47 Bauder, Julia & Cave, Libby (2025). Conceptions of data literacy in the statistics education literature, IASSIST Quarterly 49(4), pp. 1-47. DOI: https://doi.org/10.29173/iq1156 Endnotes 1 Julia Bauder is the Social Studies and Data Services Librarian and director of the Data Analysis and Social Inquiry Lab (DASIL) at Grinnell College. She can be reached by email: bauderj@grinnell.edu. 2 Libby Cave is a term librarian at Grinnell College. She can be reached by email: caveelizabeth@grinnell.edu.