A time-ordered matrix has its columns arranged chronologically by time period to determine when particular phenomena occurred. Row contents depend on what else you’re studying. This matrix is somewhat comparable with the event-listing matrix profiled above, but the time-ordered matrix emphasizes sequence, timing, and stability of processes and experiences (see Display 8.4).
Display 8.4
Time-Ordered Matrix: Changes in the CARED Innovation (a Work Experience Program)
Source: Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage Publications.
Applications
With qualitative data, you can track sequences, processes, and flows, and are not restricted to
“snapshots.” The time-ordered matrix displays time-linked data referring to phenomena that are bigger than specific “events,” so as to understand (and perhaps later explain) what was happening.
Use a descriptive time-ordered matrix like this when your data are fairly complete, to begin developing possible explanations that can be tested by moving back to the coded field notes.
Example
In our (Miles and Huberman) school improvement study, we were concerned with how an innovation changed and transformed across time during several years of implementation. We predicted that most innovations would show such changes as they were adapted to the needs of users and the pressures of the local situation.
We broke down the innovation into specific Developer Components or Other Aspects, using these as rows of the matrix. The columns of the matrix are time periods from early through later use. If a change in a component occurred during the time period, we could enter a short description of the change. A blank cell would mean no change—a nice feature that permits seeing stability, as well as change.
Display 8.4 shows how this matrix looked. The innovation, CARED, is a work experience program for high school students. The official components were those specified by the original program developer. These Developer Components do not necessarily exhaust important aspects of
the innovation, so there are rows for Other Aspects, such as time and credits or student selection.
Such aspects usually appear after initial fieldwork and direct experience with the use and meaning of the innovation.
The columns are time periods, starting with the initial planning period (because we expected changes while this relatively demanding and complex innovation was readied for use). The three succeeding school years follow.
In this case, the analyst was looking for changes in the innovation, component by component.
Those changes could be found in the coded field notes, where innovation users were asked whether they had made any changes in the innovation’s standard format. Follow-up probes asked for parts that had been added, dropped, revised, combined, or selected out for use. We used the decision rule that if a reported change was confirmed by at least one other staff member and not disconfirmed by anyone else, it should be entered in the matrix.
Analysis
Only a few analytic observations culled from the matrix are described below.
Looking across the rows of Display 8.4, you can begin to see drifts or gradual shifts expressing an accumulated tendency underlying specific changes. For example, the row “Program requirements/curriculum” shows an increasing tendency to make stiffer achievement demands on students (the tactic here is noting patterns, themes—see Chapter 11). The component “Student responsibility and time use” suggests that a process of exerting more and more control over student behavior is occurring (e.g., the accountability scheme, the system of passes, etc.).
At this point, the analyst can deepen understanding by referring back to other aspects of the field notes, notably what else people said about the changes or the reasons for them. In this example, a staff member said, “Your neck is on the block . . . the success and failures of the students rebound directly on you.” So the increased emphasis on control might come from the staff’s feelings of vulnerability and mistrust of students (tactic: noting relations between variables).
What else is happening? We can note an important structural shift in the second year: moving away from random student selection to self-selection. The field notes showed that this decision was precipitated by principals and counselors who opposed entry by college-bound students and wanted the program to be a sort of safety valve for poorly performing, alienated students. Thus, the program became a sort of “dumping ground” or “oasis” (tactic: making metaphors) for such students. But look at the “Program size” row. Though the program doubles in the second year, it cuts back substantially in the third year. In this case, severe funding problems were beginning to develop (tactic: finding intervening variables).
The report could either contain the analytic text, pulling together the strands we just wove, or present the table along with it. But Display 8.4 may be too busy and unfocused for the reader—it is more an interim analysis exhibit than a report of the findings. One way of resolving these problems is to boil down the matrix to (a) verify the tendencies observed in the initial analysis and (b) summarize the core information for the researcher and the reader.
Display 8.4 could be condensed in myriad ways. One approach is to standardize the several drifts by naming them—that is, finding a gerund such as controlling or tightening up that indicated what was going on when a change was made in the innovation (tactic: clustering) and then tying that drift to its local context, inferring what the changes mean for the case. The result appears in Display 8.5 as a summary table.
Reading the Tendency column and using the tactic of counting the number of mentions for each theme confirms the accuracy of the initial analysis. The core themes are, indeed, stiffer achievement demands (“Going academic”), more control, increased routinization (“Simplifying”), and reduced individualization (“Standardizing”). You might even try for an overarching label to typify the set—
something like “Self-protective erosion,” thus subsuming particulars into the general—and this
labeling can kick off the final analytic text, usable in the report of findings.
A scan of the Significance/Meaning for Site column helps in summarizing the underlying issues, which we identified as exhaustion, lack of local endorsement, and the discrepancy between the demands of the innovation and the organizational procedures and norms in the immediate environment.
Overall, for time-ordered matrices, consider whether the rows are organized in a sensible way.
You may need to regroup them into streams or domains. Consider whether a boiled-down, summary matrix is needed to advance your understanding or that of the reader. Also be sure the time periods chosen are a good fit for the phenomena under study. They should be fine enough to separate events that you want to keep in sequence, rather than blending them into one big pot. Use Content-Analytic Summary Tables (see Chapter 6, Display 6.20) to pull together and look at data from the display if it is too complicated.
Display 8.5
Summary Table for Verifying and Interpreting Time-Ordered Matrix: Changes in the CARED Innovation
Source: Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage Publications.
Notes
In this profile’s example, the time periods were relatively long (a full school year), but depending on the phenomena under study, they could be shorter (semesters, months, weeks, days, hours). The cell entries here were specific changes. But you can also enter specific events, such as decisions, actions, key meetings, or crises.
The rows of this matrix were components or aspects of an innovation. Many other types of rows can be used in a time-ordered matrix: roles (principal, teacher, student, parent), event types (planned/unplanned, critical actions), settings (Classroom 1, Classroom 2, playground, principal’s office, central office), or activity types (teaching, counseling, formal meetings).
By relabeling the matrix rows, you can examine time-related events or states across cases. Data
By relabeling the matrix rows, you can examine time-related events or states across cases. Data from several cases can be displayed in a way that respects chronology for assessing change (if any) in longitudinal, action, and evaluation studies. For looking at the general flow of events within a case, see the methods of event-listing matrix and event–state networks in this chapter.