| Course | IOP-5100 Industrial and Organizational Psychology |
|---|---|
| Module | Module 2 |
| Paper type | Written assignment: research methods in I/O |
| Length | About 563 words, 5 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Master of Science in Management |
| Updated | October 2026 |
Free sample paper for IOP 5100 Module 2
Shift Bidding and Absences: A Quasi-Experimental Design for a Trenton Contact Center
Student Name
Master of Science in Management, Thomas Edison State University
IOP-5100: Industrial and Organizational Psychology
Mentor Name
Month Day, Year
Shift Bidding and Absences: A Quasi-Experimental Design for a Trenton Contact Center
The Question
Unplanned absences average 9 percent of scheduled hours at our Trenton center and cost us overtime and missed service targets. Our operations director believes that letting agents bid for shifts, with priority by tenure and performance, will reduce absences because agents will work hours that fit their lives. Our sister center in Harrisburg, which serves similar clients, will keep assigned schedules for another year. That creates an opportunity to test the claim rather than assume it.
Hypothesis and Design
The hypothesis is that unplanned absence rates will fall more at Trenton than at Harrisburg in the six months after shift bidding begins, compared with the six months before.
A true experiment would assign agents randomly to bidding or assigned schedules, but that is impractical: schedules affect whole teams, and agents talk to each other. Muchinsky and Culbertson (2015) describe quasi-experiments as designs that test interventions in real settings without random assignment, accepting weaker control in exchange for realism. Our situation suits a comparison of two unmatched sites measured before and after, where only Trenton gets the new system.
The estimate of the effect is the change at Trenton minus the change at Harrisburg, which removes trends common to both sites, such as seasonal flu.
| Element | Trenton | Harrisburg |
|---|---|---|
| Before (6 months) | Absence rate measured | Absence rate measured |
| Intervention | Shift bidding begins | Assigned schedules continue |
| After (6 months) | Absence rate measured | Absence rate measured |
Measures
The main outcome is unplanned absence hours as a share of scheduled hours, drawn from payroll records. I chose records over a survey of how often agents miss work because self-reports of absence tend to be understated and because asking the same agents about both their schedule satisfaction and their absences invites common method bias. Podsakoff et al. (2003) show that when the same source and method supply both predictor and outcome, shared influences such as mood or a desire to appear consistent can inflate relationships. A short survey of schedule satisfaction will be collected separately as a possible explanation, not as the outcome.
Threats to Validity
Shadish et al. (2002) describe threats that can make a study's conclusions wrong. Four matter here. History: another change at one site, such as a new client, could affect absences; I will log events at both sites. Selection: the sites differ in workforce age and commute patterns; I will compare their pre-period absence trends and adjust for tenure. Attrition: if agents who dislike bidding leave, the remaining group may look better; I will analyze all agents present at the start. Regression to the mean is less of a concern because Trenton was not chosen for an unusually bad period.
Ethics
Agents will be told that absence data, which the company already collects, will be analyzed in aggregate to evaluate the new system. Survey responses will be anonymous and voluntary. No individual's absence data will be reported to supervisors as part of the study.
Analysis
I will compare monthly absence rates using a difference-in-differences model with site, period and their interaction, controlling for tenure and month. A reduction of at least one percentage point at Trenton relative to Harrisburg would justify extending bidding.
Conclusion
A quasi-experiment with a comparison site, records-based measures and attention to validity threats lets the center test a plausible idea before betting both sites on it.
References
Muchinsky, P. M., & Culbertson, S. S. (2015). Psychology applied to work (11th ed.). Hypergraphic Press.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
What the IOP 5100 Module 2 instructions ask for
Eight written assignments carry 48 percent of IOP-5100; with no module dates in the syllabus, one is placed here beside research methods. Expect to state a workplace question as a testable hypothesis, choose a design and justify it, select measures and explain their reliability and validity, address threats to the conclusions and ethical issues, and describe the analysis. Muchinsky and Culbertson's chapter on research methods supplies the concepts. A real question from your workplace makes the design concrete, and a table of the design helps the reader follow it. Say before collecting data which outcome would back the claim and which would sink it.
Inside the IOP 5100 Module 2 example
The paper starts with a real disagreement: an operations director's belief that shift bidding will reduce absences. It turns the belief into a hypothesis and explains why random assignment is impractical, choosing a nonequivalent comparison group design shown in a table. The measures section prefers payroll records to self-report, citing Podsakoff and colleagues on common method bias. Four threats from Shadish, Cook and Campbell are addressed one by one. A short ethics section covers consent and confidentiality, and a difference-in-differences analysis with a decision threshold closes the plan. One margin comment defends the choice of payroll data. The ethics paragraph keeps individual absence records away from supervisors.
IOP 5100 Module 2 rubric: what earns full marks
A research design paper earns marks for a testable claim, a design defended against alternatives, well-chosen measures, care with validity and ethics, and clean APA form. Papers earn credit for explaining why a design was chosen over alternatives, including why a true experiment was not feasible. Choosing measures that avoid known biases shows methodological judgment. Taking each rival explanation in turn, with a remedy, proves more than defining them. An analysis plan with a decision rule shows that the study is meant to inform action, which is the applied purpose of I/O research. Mentors also look for designs that could produce an answer the organization did not expect, since a study that can only confirm a belief is not a test.
IOP 5100 Module 2 help from the desk
Students lose points by describing research methods in general without designing a study, by choosing a survey for every outcome or by listing validity threats without addressing them. Start with a question your organization actually argues about. State a hypothesis that could be wrong, choose a design that fits what is possible, measure the outcome in a way that avoids bias and handle each major threat specifically. Add how you will analyze the data and what result would change a decision. Request a free first sample of your IOP-5100 Module 2 assignment built on your own workplace question. Draw the design as a small table of groups and time points so the logic of the comparison is visible at a glance.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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IOP 5100 Module 2 questions, answered
Where can I find a free IOP 5100 Module 2 sample paper?
The paper above designs a quasi-experiment on whether shift bidding reduces absences at a Trenton contact center.
What is a quasi-experiment?
A test of a change in which people are not randomly assigned, so the researcher compares the changed group with a similar unchanged one, ideally before and after.
What is common method bias?
Inflated relationships that arise when the same source or method, such as one survey, measures both the predictor and the outcome.
What are threats to internal validity?
Rival explanations such as history, selection, attrition and regression to the mean that could produce the result instead of the intervention.
How many written assignments does IOP-5100 have?
Eight written assignments, worth 48 percent of the final grade together.