PSYCH 629 Week 1 I-O Psychology History and Research Methods Example

Did the Four-Day Week Work? I-O Psychology's Roots and the Research Design a Solar Installer Needs to Answer the Question

[Student Name]

University of Phoenix

PSYCH/629: Introduction to Industrial/Organizational Psychology

Week 1 Assignment

[Instructor Name]

[Date]

The solar company, its crews and the pilot figures are composites written for a model paper; historical accounts and research findings come from the sources listed.

Industrial and organizational psychology applies psychological science to work. Its history shows how the field learned, often through mistakes, which questions it could answer and how. This paper traces that history briefly and applies its lessons to a question at my workplace: whether a new work schedule improved performance.

The Question

I work as an HR generalist at Sunridge Solar, a residential solar installation company near Henderson, Nevada, with about 140 employees, including sixty installers in twelve crews. Last spring, the owner moved all crews from five eight-hour days to four ten-hour days, partly to avoid the worst afternoon heat in summer by starting at dawn. After three months, a staff survey showed that eighty-seven percent of installers preferred the new schedule, and the owner declared that productivity had improved. The finance director was not convinced. I was asked how we could know.

Scientific Management and the Search for Efficiency

Taylor (1911) argued that management could be made scientific by studying each task, timing its elements, determining the one best way to perform it, selecting and training workers for it and paying them according to output. His studies of shoveling and pig iron handling at Bethlehem Steel became famous examples. Scientific management increased efficiency in many settings, but it treated workers mainly as parts of a production system and provoked resistance from labor. Its legacy survives in job analysis, work measurement and standard procedures, including the install time standards our company uses.

Testing, War and the Birth of Personnel Psychology

Early psychologists such as Hugo Münsterberg applied experimental methods to selecting workers, and during World War I psychologists developed the Army Alpha and Beta tests to classify recruits. World War II expanded the field again, with work on selection, training and the design of equipment. These efforts established the industrial side of the field, focused on fitting people to jobs through measurement.

The Hawthorne Studies and the Human Side of Work

Between 1924 and 1932, researchers at Western Electric's Hawthorne plant near Chicago studied how lighting, rest breaks and work hours affected output. The early illumination experiments reportedly found that output rose whether lighting increased or decreased, which led to the idea that attention itself improves performance, later called the Hawthorne effect. The broader program, including interviews and observation of work groups, helped launch the human relations movement and the organizational side of I-O psychology, which studies attitudes, groups and leadership.

The famous effect, however, rests on thin evidence. Levitt and List (2011) located the original illumination data, long thought lost, and reanalyzed them. They found that the patterns had been described more dramatically than the data supported; output did rise in ways consistent with a modest effect of being studied, but the evidence was weak, and other factors such as seasonal patterns could explain some changes. For a practitioner, the lesson is that an origin story repeated in every textbook still has to earn belief from its data.

The most famous result in I-O history turned out to be a story the data only half told.

The Field Today

Modern I-O psychology covers selection and assessment, performance management, training, motivation and attitudes, leadership, teams, occupational health and organizational development. Its methods include surveys, field and laboratory experiments, quasi-experiments, observation and meta-analysis, which combines results across studies. The field emphasizes evidence-based practice, ethics and attention to fairness.

Why the Survey Is Not Enough

Our survey shows that installers like the schedule, which matters for retention and morale. But the owner's conclusion about productivity rests on a weaker inference. Podsakoff et al. (2003) reviewed sources of common method bias, the systematic error that arises when variables are measured with the same method or from the same respondents, such as asking people in one survey both how satisfied they are and how productive they are. Respondents' desire to appear consistent, their mood and the wording of items can inflate relationships. The authors recommended obtaining measures from different sources, separating measurements in time and using statistical controls. Our survey asked installers whether they were more productive, which they may sincerely believe partly because they like the schedule.

Threats in Our Situation

Several other explanations could account for any change in output. History effects: the pilot began in spring, when demand rises and weather is mild, so installs per crew might have risen regardless. Reactivity: crews knew the owner was watching the pilot, a possible small Hawthorne-type effect. Selection: two crews lost members during the pilot, and replacements were experienced hires. And regression to the mean: the owner launched the pilot after an unusually slow winter.

A Better Design

Randomly assigning individual installers to schedules is impractical, since crews work together. Grant and Wall (2009) made the case that field studies without random assignment can still be persuasive if the researcher builds in protections, for instance starting the change at different times for different units, keeping some units as a yardstick and taking several readings before and after. Following that guidance, I proposed a stepped design for next season. Six crews return to the five-day schedule for two months while six continue the four-day schedule, and then the groups switch. We will compare the groups using company records rather than self-reports: installs completed per crew-day, customer callbacks for workmanship problems, recordable injuries and heat-related incidents, overtime hours and voluntary turnover. We will also compare each month with the same month in the previous year to account for seasonal demand.

Choosing Measures That Matter

The measures deserve as much care as the design. Installs per crew-day is the owner's main interest, but it can be gamed by rushing, which is why callbacks for workmanship problems must be tracked beside it. Heat-related incidents and recordable injuries capture the safety purpose that motivated the change in the first place. Overtime hours show whether ten-hour days quietly stretch into eleven. Voluntary turnover over the following year shows whether the schedule helps the company keep skilled installers, who take months to train. Satisfaction still matters, so the survey will continue, but it will be one measure among several rather than the proof of success. Agreeing on these measures with the owner and the finance director before the study begins prevents anyone from choosing the favorable numbers afterward.

Ethics and Fairness

Every crew will hear why the comparison is being run, and neither schedule will change anyone's hourly rate. Data will be reported by crew, not by individual. Crews will have input into which group they join first, and the schedule that performs better on safety will be adopted regardless of other results.

What We Expect to Learn

If the four-day schedule matches or improves installs and callbacks and reduces heat incidents, we will have strong evidence to keep it. If productivity drops but satisfaction and safety improve, the decision becomes a trade-off for leadership to weigh with honest data.

Conclusion

I-O psychology grew from efforts to make work more efficient and from the discovery that people's attitudes and groups matter. Its history also shows how easily a compelling story can outrun the evidence. A schedule change that workers love deserves a fair test using objective records, a comparison and attention to threats that a single survey cannot rule out.

References

Grant, A. M., & Wall, T. D. (2009). The neglected science and art of quasi-experimentation: Why-to, when-to, and how-to advice for organizational researchers. Organizational Research Methods, 12(4), 653-686. https://doi.org/10.1177/1094428108320737

Levitt, S. D., & List, J. A. (2011). Was there really a Hawthorne effect at the Hawthorne plant? An analysis of the original illumination experiments. American Economic Journal: Applied Economics, 3(1), 224-238. https://doi.org/10.1257/app.3.1.224

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

Taylor, F. W. (1911). The principles of scientific management. Harper & Brothers.