From conceptual model to measurement
Chapter 9: Measurement and Construct Validation — Video 2
https://ghrbook.com/videos/from-conceptual-model-to-measurement/

[Slide 1] The first step in planning study measurement is to decide what to measure. I always start this process by creating a conceptual model, such as a DAG or a theory of change. Conceptual models can help you identify what data you must collect or obtain to answer your research question.
[Slide 2] In an earlier chapter I introduced directed acyclic graphs, or DAGs, as tools for mapping causal assumptions. Here I want to show how a DAG doubles as a measurement planning tool: every node tells you something about what data you need to collect. Consider the HPV vaccination example from that chapter. Researchers wanted to estimate the causal effect of HPV vaccination on sexual behavior.
[Slide 3] This is the DAG for that question, with the minimum adjustment set highlighted in blue: the smallest group of variables that blocks all confounding paths between the exposure and the outcome.
[Slide 4] The three highlighted nodes, health beliefs, parental attitudes, and socio-economic status, are the variables we must measure or obtain data on to close the backdoor paths. The two drawn with a dashed outline, vaccination campaigns and relationship context, we don't need for our primary analysis, because they don't create backdoor paths.
[Slide 5] The DAG has turned a causal inference problem into a data collection checklist. And that checklist forces hard questions: how will we measure health beliefs? Is there a validated instrument for parental attitudes in our study population? Can we get reliable socio-economic data? If the answer to any of these is no, we know before we start that our analysis will have gaps, and we can plan accordingly.
[Slide 6] DAGs are powerful for observational studies where confounding is the central concern. But for intervention studies, where you're testing whether a program or treatment works, a different kind of conceptual model is often more useful for measurement planning: the logic model, sometimes called a theory of change. A logic model maps the hypothesized causal chain from what you put into a program to what you expect to get out of it. It reads left to right, like a story: resources go in, activities happen, products come out, and if everything works as intended, change follows.
[Slide 7] Inputs, then activities, then outputs, then outcomes, then impacts. The measurement planning payoff is similar to the DAG: each box in the chain represents something you might need to measure. But whereas a DAG tells you what to control for, a logic model tells you what to monitor and evaluate. It answers a different set of questions: did we spend what we planned to spend? Did we deliver the intervention as designed? Did participants actually receive it? And did the outcomes change?
[Slide 8] Here is a plausible logic model for the trial. Inputs on the left, impacts on the right, and each box in between represents something you might need to measure.
[Slide 9] Activities are what you actually do with your inputs: the services delivered, trainings conducted, or interventions implemented. In a logic model, activities represent the action that's supposed to produce change. The main activities here were psychotherapy for patients and supervision of lay counselors. The program was designed to be delivered in an individual, face-to-face format, by telephone when necessary, over 6 to 8 weekly sessions each lasting 30 to 40 minutes. Supervision consisted of weekly peer-led group supervision and twice monthly individual supervision.
[Slide 10] Outputs are the direct, countable products of your activities: how many sessions delivered, how many people trained, how many materials distributed. Outputs tell you whether you did what you said you would do, but they don't tell you whether it worked. In this trial the authors counted the number of sessions delivered to patients in the treatment arm, as well as the number of patients who completed the program. Sixty-nine percent had a planned discharge. Presumably they also tracked the number of counselors trained and supervision sessions conducted.
[Slide 11] In intervention studies like this, it's important to determine if the intervention was delivered as intended. This is called treatment fidelity, and it's a measure of how closely the actual implementation of a treatment or program reflects the intended design. The study authors measured fidelity in several ways, including external ratings of a randomly selected 10 percent of all intervention sessions. An expert not involved in the program listened to recorded sessions and compared session content against the manual. They also had counselors document the duration of each session.
[Slide 12] Low treatment fidelity usually results in an attenuation, a shrinking, of treatment effects, which is a threat to internal validity. If the study shows no effect but treatment fidelity is low, the null result may not be valid. Implementation failure rather than theory or program failure could be to blame. Low fidelity is also a threat to external validity, because it is not possible to truly replicate the study.
[Slide 13] Outcomes are the changes you hope to see as a result of your activities: the so-what of your work. Unlike outputs, which count what you did, outcomes measure whether it made a difference. The hypothesized outcome in this study was a reduction in depression. The authors registered two primary outcomes: depression severity, assessed with the modified Beck Depression Inventory, and remission from depression, defined by a score below ten on the Patient Health Questionnaire, both assessed three months after enrollment.
[Slide 14] Impacts are the longer-term changes you believe will result from achieving your outcomes, and they're often not directly tested in a single study. They unfold over years or decades, making them difficult to measure within a typical research timeframe. The authors assumed that the long-term impact of reducing depression at scale would be improvements in quality of life for patients and their families, increased workforce productivity, and a reduction in costs to society.
[Slide 15] Logic models and DAGs provide a solid foundation for measurement planning. If you or I were designing this study, the logic model would tell us that we need to collect or obtain data on expenditures, on measures of treatment fidelity, on counts of therapy sessions completed and supervision sessions held, and on measures of depression and several secondary outcomes. Generating this list is the first step. The next step is figuring out the specifics of measurement.