Longitudinal research involves collecting data from the same entities on two or more occasions. Almost all organizational theories outline a longitudinal process in which one or more variables cause a subsequent change in other variables. However, the majority of empirical studies rely on research designs that do not allow for the proper assessment of change over time or the isolation of causal effects. Longitudinal research begins with longitudinal theorizing. With this in mind, a variety of time-based theoretical concepts are helpful for conceptualizing how a variable is expected to change. This includes when variables are expected to change, the form or shape of the change, and how big the change is expected to be. To aid in the development of causal hypotheses, researchers should consider the history of the independent and dependent variables (i.e., how they may have been changing before the causal effect is examined), the causal lag between the variables (i.e., how long it takes for the dependent variable to start changing as a result of the independent variable), as well as the permanence, magnitude, and rate of the hypothesized change in the dependent variable. After hypotheses have been formulated, researchers can choose among various research designs, including experimental, concurrent or lagged correlational, or time series. Experimental designs are best suited for inferring causality, while time series designs are best suited for capturing the specific timing and form of change. Lagged correlation designs are useful for examining the direction and magnitude of change in a variable between measurements. Concurrent correlational designs are the weakest for inferring change or causality. Theory should dictate the choice of design, and designs can be modified and/or combined as needed to address the research question(s) at hand. Next, researchers should pay attention to their sample selection, the operationalization of constructs, and the frequency and timing of measures. The selected sample must be expected to experience the theorized change, and measures should be gathered as often as is necessary to represent the theorized change process (i.e., when the change occurs, how long it takes to unfold, and how long it lasts). Experimental manipulations should be strong enough to produce theorized effects and measured variables should be sensitive enough to capture meaningful differences between individuals and also within individuals over time. Finally, the analytic approach should be chosen based on the research design and hypotheses. Analyses can range from t-test and analysis of variance for experimental designs, to correlation and regression for lagged and concurrent designs, to a variety of advanced analyses for time series designs, including latent growth curve modeling, coupled latent growth curve modeling, cross-lagged modeling, and latent change score modeling. A point worth noting is that researchers sometimes label research designs by the statistical analysis commonly paired with the design. However, data generated from a particular design can often be analyzed using a variety of statistical procedures, so it is important to clearly distinguish the research design from the analytic approach.
The emergence of co-occurring adolescent polysubstance use and depressive symptoms: A latent growth modeling approach
