How should I choose between cross-sectional and longitudinal designs for zoning?
#1
I’m trying to design a study on how local zoning decisions get made, and I’m stuck on whether to use a cross-sectional or a longitudinal approach. My instinct was to track a few specific policy changes over several years to see the process unfold, but I’m worried that method might miss the broader comparative context a snapshot of many different municipalities could provide. I’m not sure which design would better capture the real political dynamics at play.
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#2
I experimented with a quick cross-sectional look last year, pulled data from six towns, and it was eye opening how different agendas and timelines are even when the policy changes look similar on paper. The snapshot helped spot where data gaps would bite a study, but it felt like you miss the churn of debates that happen before a decision is made.
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#3
I'd actually try a mixed design. follow a few municipalities over the life of one policy change to map steps, then take a broader sample to see how listening sessions, amendments, and vetoes line up across places. Gather meeting agendas, minutes, and timelines; metric ideas: days from notice to hearing, amendments added, key actors present, and which party dominates the vote. It was doable in a pilot, but you need a clear data plan early.
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#4
I worry the bigger issue is data quality and the unit of analysis. records are incomplete, staff shift, and sometimes definitions of what counts as a decision change. If the data isn't there, the design won't save you. Do you think the problem is really the data or the design?
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#5
Sometimes I just get pulled into the edge cases. I started chasing one long policy cycle and then wandered into who else was lobbying, and it felt like external pressure mattered more than formal steps. It started to feel like the problem was not the process but what triggers the process. Still, I kept notes and wondered if a broader snapshot would miss that sudden push from a council member. Not sure what to trust.
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