Research Design and Methods

 Describe a dilemma you are currently facing, and the variables involved. Would this dilemma be best resolved by using a correlational analytical approach or a causal-comparative analytical approach?

Then, choose a peer(s) response and make a case for using the opposite analytical approach (e.g., If your peer(s) advocates using a test of relationships, such as simple regression analysis, to address their dilemma, make a case for using a test of differences, such as the t-test).

 

A dilemma I’m facing is how to prioritize limited facilities resources on an inpatient unit at Penrose Hospital (Colorado) for example do we put our time and dollars into reactive work orders that keep popping up (leaks, temperature complaints, equipment downtime), or do we shift more aggressively toward preventive upgrades that reduce risk over time (PM compliance, high-touch cleaning reliability, airflow/pressure checks, and targeted room refreshes)?  The variables in that decision are the size of the backlog, staff availability, budget, downtime impact, patient acuity, unit census, complaint volume, and safety indicators that matter to leadership (for example, infection trends, environmental deficiencies, or repeated utility interruptions). This has been a total pain that we can't seem to organize well with a lack of capital funding, fte allocations etc.

I think my dilemma is best addressed with a causal-comparative (ex post facto) approach because the “groups” already exist in practice: units or time periods where proactive facility/EVS interventions were implemented versus units or periods where they were not, and then outcomes can be compared. In causal-comparative research, you categorize people or settings into groups based on a preexisting variable and compare them on an outcome, rather than manipulating conditions like a true experiment (Ary et al., 2010, pp. 45, 333–336). A correlational approach would be useful if I only wanted to see whether two variables move together (like higher PM completion rates being associated with fewer equipment failures), but my real question is closer to “what differences show up between two existing approaches,” which aligns more cleanly with causal-comparative design logic (Ary et al., 2010, pp. 45, 351–352).

References

Ary, D., Jacobs, L. C., & Sorensen, C. K. (2010). Introduction to research in education (8th ed.). Wadsworth, Cengage Learning.