Skip to main content
Social Sci LibreTexts

9.3: Navigating Qualitative Data Collection

  • Page ID
    76239
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)
    Learning Objectives

    By the end of this section, you will be able to:

    • Understand the importance of anonymity and confidential to ethical data collection
    • Consider how a reflexive approach to data collection can minimize bias and open ourselves to new ways of thinking

    As the above section points out, engendering trust can provide access to individuals, communities and insights that would be otherwise difficult to obtain. But with such access also comes a great deal of responsibility when it comes to data collection. For instance, an important ethical consideration is offering to conduct an interview, survey, or observation under the condition of confidentiality. This not only allows human subjects the comfort to speak their truth but takes seriously the principle of “first do no harm,” rather than treating them as means to our own academic ends.

    It is not uncommon that a researcher may come to the realization that one or a number of the participants in their study may face some kind of retribution, embarrassment, or worse as a result of our study, even if the participant may not have realized it at the time of consent. Once again, we are confronted with an ethical dilemma: do we cite the source knowing that it will give more credibility to our study or do we anonymize, if not completely omit this information, knowing the potential harm that could befall our participant(s)? This is not an easy decision, but as we have mentioned, the ethical researcher is encouraged to be cautious rather than risk human harm. Indeed, we are asking our subjects to help us learn and create knowledge. Therefore, it would not only be selfish but unethical to knowingly put them in harm’s way for the sake of our study, if we know there is even a possibility of negative repercussions.

    Beyond minimizing the potential of physical or psychological harm that could befall our human subjects, we must also consider the epistemic violence, or harm that can come as a result of local knowledge being displaced and/or distorted by our alternative own frameworks, concepts and ways of knowing (i.e. epistemology) (Spivak 2010). For example, political scientists also adjust their instruments data collection methods in the field based on how different participants interpret the phenomenon under investigation, and the meanings they ascribe to them. Similar to Schaffer’s study of “demokrassi” in Senegal, how interviewees understand key concepts and ideas under investigation, in this case democracy, frequently vary from language used in academic debates (Schaffer 2000). It can be incredibly frustrating when our survey instruments, typically rooted in theoretical literature or previous studies, do not mesh with what participants are articulating in the field. It is therefore not uncommon for scholars, either subconsciously or otherwise, to gather data in such a way that can mistakenly confirm hunches, and related questions, which animate their projects.

    Yet, the desire to confirm what we are looking for can unwittingly bias the responses of our interviewees, based on how one frames their questions and any perceived position of authority or power of the interviewer. Ethically, avoiding the temptation to impose our conceptual apparatus on human subjects can discipline the researcher’s thinking, help them productively rethink initial hunches in useful ways, avoid the presumptive authority of academic concepts, and be open to a constant integration and revaluation of them (Godrej 2011). For instance, participant observation is often an essential tool for providing evolving survey instruments with the appropriate language for bridging academic debates with what our interviewees are trying to tell us. However, a key dimension of such an approach is a commitment to reflexivity regarding the ways in which one’s personal identity, perception by others, and worldview may affect the way they are able to gather and analyze one’s data (Yanow and Schwartz-Shea 2011). This may involve “interrogating forms of inclusion and exclusion and breaking down boundaries. Likewise, it may involve listening for silences and sometimes responsibly sustaining those silences, depending on the context (Ackerly and True 2010; Cecelia Lynch 2013).”

    Reflexive research also includes an awareness of the distorting effects that arise from ones’ location in their academic field, one’s personal relationship to their subjects, and acknowledging the fact that they are inextricably involved in the social processes under observation (Cecilia Lynch 2014). Yet, as one learns how to mitigate potential response bias (the tendency of participants to answer our question inaccurately due to the wording of questions or how their answer will be perceived by the researcher) and confirmation bias (the tendency of researchers to interpret data in such a way that it confirms their existing beliefs), our unique position in the field can also help bridge boundaries between academic debates and our research subjects, along with those between theory and practice. Ethically engaging the voices, stories, and insights of human subjects thus have both material and epistemological consequences. Specifically, allowing researchers not only contribute to key debates in political science, but also share what we have learned with those who helped co-generate this new knowledge.


    This page titled 9.3: Navigating Qualitative Data Collection is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Josue Franco, Charlotte Lee, Kau Vue, Dino Bozonelos, Masahiro Omae, & Steven Cauchon (ASCCC Open Educational Resources Initiative (OERI)) via source content that was edited to the style and standards of the LibreTexts platform.

    • Was this article helpful?