Explanatory sequential design (Mixed methods#3)

Explanatory sequential mixed methods put numbers first and then use interviews or other qualitative work to make sense of what the statistics can’t fully explain. In this design, a quantitative phase identifies trends, patterns, and even outliers; a follow-up qualitative phase then targets those specific results to clarify why they happened, how participants interpret them, or why certain cases don’t match the broader dataset. The payoff is a more complete explanation: quantitative findings establish “what,” while qualitative data helps answer “why” or “how,” especially when the quantitative results leave gaps.

This approach is most attractive when researchers are comfortable leading with quantitative methods and have the time and resources to run two distinct phases. It also fits situations where an unexpected quantitative pattern emerges midstream—after analyzing the data, researchers realize they need participant perspectives to explain philosophical worldviews, assumptions, or other underlying meanings that the numbers alone can’t capture. The design’s logic is inherently adaptive: the second phase is shaped by what appears in the first phase, so the qualitative component is not just an add-on but a targeted response to the quantitative results.

Because the two phases draw on different research traditions, the design often aligns with different philosophical assumptions. The quantitative strand is associated with postpositivism, while the qualitative strand leans toward constructivism, reflecting an interest in how individuals construct meaning about the phenomenon under study. Practically, the workflow starts with collecting and analyzing quantitative data, then using those results to plan purposeful sampling for the qualitative phase—selecting participants who can shed light on the trends or outliers identified earlier. Researchers then collect and analyze the qualitative data, interpret how well it explains the quantitative findings, and reflect on what the combined evidence reveals.

Reporting is another decision point. Many conventions separate results into distinct sections—quantitative results followed by qualitative results—but some researchers interleave them, inserting qualitative findings alongside quantitative ones to strengthen the overall argument. Either way, the key is to make the explanatory link explicit: qualitative insights should be tied back to the specific quantitative patterns they were designed to clarify.

Implementation brings real constraints. Researchers must decide who to recruit for the second phase and how, even though the exact questions and participant needs may not be fully known until the quantitative analysis is complete. This can complicate early-stage proposals because the design is “emerging”: the second phase depends on the first. Time is also critical, since the sequential structure requires running two phases end-to-end.

Despite these challenges, the design’s advantages are straightforward. It produces rich, triangulated evidence by combining qualitative and quantitative data, and it leverages responsiveness to data—allowing the study to become more in-depth and, ultimately, more interesting as the explanation develops from the first phase’s findings.