In the previous post, I detailed a few different types of research designs. In this post I will talk about an important element in research design: Time.
According to Mitchell and James, time is treated as a commodity that can be broken into meaningful segments or blocks. It flows evenly and consistently, it’s precise and quantifiable, and is ordinal.
Time is an important variable to consider in theory because theory concerns about causal relationship between x and y and time is a marker. In relationship between X and Y, there are time lag, duration, rate of change, reciprocal causality and non-linear cyclical and oscillating issues. Thus, time in theory is significant. By the same token, time is important when designing a research study because the measurement of when x and y occur and when they are measured would make significant differences in the result. If we ignore time in the theory-building phase, it would make bad theory. If we ignore time in the designing phase, it may lead to bad design and misleading results.
Five major ways in which theory informs method with respect to time:
- First, we need to know the time lag between X and Y. How long after X occurs does Y occur?
- Second, X and Y have durations. Not all variables occur instantaneously.
- Third, X and Y may change over time. We need to know the rate of change.
- Fourth, in some cases we have dynamic relationships in which X and Y both change. The rate of change for both variables should be known, as well as how the X,Y relationship changes.
- Finally, in some cases we have reciprocal causation: X causes Y and Y causes X. This situation requires an understanding of two sets of lags, durations, and possibly rates.
There are several ways to consider time into a research. First, we can think of the time of the measurement. If we know the time relation between variables of interest, we can consider time easily. However, we do not know it most of the time because theory rarely specifies time issues. By posing some questions, we can address this issue. When should I start measure X (start time issue)? How stable X is when measuring (stable issue)? When does Y appear after X (lag issue)?
Second, we can address time issue in our study considering the frequency of measurement. Y may change over time systematically, but not particularly complex. To assess rate of change we need multiple assessments through theoretical consideration of intra individual change, inter individual change and contextual change.
Lastly, we want to examine the issue of measure stability. Changes in the assessment of a variable over time can be due to random error, systematic error or systematic change. For this issue, test-retest assessment can provide information on the stability of X and Y and time-series (numerous observations on a small number of subjects) designs assess sources of error over time.
Some ways to think about time include:
- Construct Mitchell’s Moderation by Causal Cycle curve: illustrated periods of time in which X,Y interact
- Equilibration period: period it takes X to affect Y and for Y to reach state of constancy of state of equilibration
- Equilibrium period: when Y reaches state of equilibration, Y is said to enter an equilibrium type condition. The scores of Y may continue to change in this period, but the changes are small and constancy is resumed fairly quickly. This is when Y should be measured
- Entropic period: changes in Y are completely uncertain with respect to given set of measurements on Y. This is final state of causal cycle. This is when you need to stop measuring Y.
- Draw diagram for relationships between X,Y in order to get an idea of issues such as lags, change and reciprocal causality
- Consider amount or rate of change. How much does X effect Y? Is this rate constant? If not, do you have to measure Y multiple times?
- Specify time in your research design before you begin
- The Frequency of Measurement–As Kelly and McGrath (1988) point out, we need at least three assessments to look at a curvilinear relationship; four for oscillation; and perhaps more for rhythms, spirals, and cycles.
- Stability (reliability)–Test-retest assessments (if variables are assessed during a steady-state or equilibrium period) can provide information on the stability (reliability) of X and Y, and time-series designs, as we mentioned earlier, also assess sources of error over time.