Transforming research methods education through data science literacy
| Published date | 01 July 2023 |
| DOI | http://doi.org/10.1177/01447394221084488 |
| Author | Michael Overton,Stephen Kleinschmit |
| Date | 01 July 2023 |
| Subject Matter | Articles |
Article
Teaching Public Administration
2023, Vol. 41(2) 149–169
© The Author(s) 2022
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DOI: 10.1177/01447394221084488
journals.sagepub.com/home/tpa
Transforming research methods
education through data science
literacy
Michael Overton
Department of Politics and Philosophy, University of Idaho, Moscow, ID, USA
Stephen Kleinschmit
Department of Public Administration, University of Illinois at Chicago, Chicago, IL, USA
Abstract
Mass adoption of advanced information technologies is fueling a need for public servants
with the skills to manage data-driven public agencies. Public employees typically acquire
data skills through graduate research methods courses, which focus primarily on research
design and statistical analysis. What data skills are currently taught, and what content
should Master of Public Administration (MPA) programs include in their research method
courses? We categorized research method course content in 52 syllabi from 31 MPA
programs to understand how data skills are taught in public administration. We find that
most graduate programs rely on research methods more suited for academic and policy
research while lacking the data skills needed to modernize public agencies. Informed by
these results, this work presents the Data Science Literacy Framework as a guide for
assessing and planning curriculum within MPA programs.
Keywords
research methods, data science, data science literacy, data-skills gap, data literacy
Introduction
Public agencies are increasingly reliant on and inundated by large volumes of complex,
continuously created data known as Big Data (Secundo et al., 2017). Public sector
professionals need new data skills to effectively use Big Data to inform decision-making
Corresponding author:
Michael Overton, Department of Politics and Philosophy, University of Idaho, 875 Perimeter Drive, Moscow,
ID 83844, USA.
Email: Moverton@uidaho.edu
(Secundo et al., 2017;Mergel, 2016). Collecting, cleaning, managing, and deriving
meaning from data are quickly becoming necessary skills, while traditional research
methods and inferential statistics are becoming less valuable (Baumer, 2015;Cobb,
2015). Consequently, educators must consider revising existing research methods cur-
riculum and teaching students’data skills to ensure relevance (Miller, 2014). What data
skills are currently taught, and what content should Master of Public Administration
(MPA) programs include in their research method courses?
There is a clear need for practitioners with data science skills in the public sector
(Secundo et al., 2017). Using data effectively requires that employees possess data and
computational skills, but there are few practical paths for MPA students to acquire them.
Public Administration (PA) programs are ill-equipped to offer data science courses
because faculty often lack computational training, and the prohibitive technicality of data
science makes adding it as a non-focal point of curricula difficult. Data science programs
housed in computer science and statistics departments are available at many universities
but are largely inaccessible and irrelevant to PAstude nts due to burdensome pre-requisites
and pedagogical approaches tailored almost exclusively to private sector applications
(Kleinschmit, 2019; Cobb, 2015).
To create a foundation for teaching data skills within PA, we first assess what skills are
currently taught. We conducted a content analysis of 52 research method syllabi from
31 MPA programs in the United States to accomplish this goal. Course content is then
classified using a typology of data skills, the Data Science Literacy Framework (DSLF),
which provides data science and data literacy content for research method instructors to
consider including in their courses. The strengths and weaknesses of PA data education is
evaluated through this classification system while offering prescriptive guidance for
incorporating data competencies throughout research methods sequences.
This work begins with a discussion of the data skills gap, then considers how data
science and literacy address the gap but are incomplete individually. Next, the DSLF and
its domains are outlined and explained. Following the presentation of the DSLF, the meth-
odology and results sections detail the analytical approach and findings, then the manuscript
concludes with a discussion of potential revisions to MPA research method curricula.
The data skills gap
There is a considerable difference between the data skills public sector employees have
and the data skills public organizations need resulting in a data skills gap (Chinien and
Boutin 2011). City managers identified the lack of data skills (i.e., internal capacity and
technical expertise) as significant barriers to adopting and implementing smart city
technologies (ICMA and Smart Cities Council, 2016). Additionally, among a survey of
federal managers, 96% of respondents perceived a discernible data skills gap at their
agencies, identifying a lack of adequate training, cultural norms surrounding technology,
data accessibility issues, and budget restraint as challenges (Fiorenza, 2016). Other
research suggests management is partially to blame as a “lack of executive understanding”
was cited by 37% of public organization leaders as an obstacle to the effective use of data
to achieve goals and mandates (Mullich, 2013).
150 Teaching Public Administration 41(2)
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