Field Studies

Field studies are also effective ways to learn about what is truly going on within organizations. There are survey field studies like the one above, but more compelling evidence comes from field studies that employ an experimental design. Here you would assign half the employees at your company to the goal setting condition and the other half to the control group condition. The control group wouldn’t get any information on goal setting but the treatment group would. If you found that the treatment group was more effective than the control group, you could tell your boss that goal setting works.

Laboratory Studies

OB researchers are often interested in basic research questions such as “Can we show that goal setting increases performance on a simple task?” This is how research on goal setting started, and it is also how we can establish the conditions under which it works more or less effectively. Again, to address this, researchers may conduct a lab study in which one group is assigned one condition and the other group is assigned the control condition (generally the control condition involves no change at all). You may even have been involved in a lab study during your time at your university. One of the most important concepts to understand with lab studies is that they give the researcher a great deal of control over the environment they are studying but do so in a less “realistic” way, since they are not studying real employees in real work settings. For example, in a lab study, a researcher could simulate hiring and firing employees to see if firing some employees affected the goal-setting behavior of the remaining employees. While this wouldn’t be legal or ethical to do in a real organization, it could be a compelling lab study. At the same time, however, firing someone in a lab setting does not necessarily carry the same consequences as it would in real life.

Case Studies

Case studies are in-depth descriptions of a single industry or company. Case writers typically employ a systematic approach to gathering data and explaining an event or situation in great detail. The benefits of case studies are that they provide rich information for drawing conclusions about the circumstances and people involved in the topics studied. The downside is that it is sometimes difficult to generalize what worked in a single situation at a single organization to other situations and organizations.

Meta-Analysis

Meta-analysis is a technique used by researchers to summarize what other researchers have found on a given topic. This analysis is based on taking observed correlations from multiple studies, weighting them by the number of observations in each study, and finding out if, overall, the effect holds or not. For example, what is the average relationship between job satisfaction and performance? Research shows that, looking across 300 studies, the relationship is moderately strong.^[1] This is useful information because for years people had thought that the relationship did not exist, but when all the studies to date were examined together, the original beliefs about the satisfaction–performance relationship deteriorated. The advantage of meta-analysis is that it gives a more definitive answer to a question than a single study ever could. The downside is that meta-analysis is only possible if sufficient research has been done on the topic in question.

Measurement Issues in OB

Another important thing to understand is the difference between reliability and validity. Imagine you own a trucking company. A major component in trucking is managing the weight of different cargo. If you had a scale that gave you the same weight three times, we would say that was a very reliable scale. But, if it turns out the weights given are in kilograms instead of pounds, it would not be a valid measure if you charge for delivery by the pound.

Finally, much of management research addresses correlations between two concepts rather than actual causation. Correlation simply means that two things co-vary. For example, it would be inaccurate to assume that because 99% of the people who died this year also drank water, consuming water kills people. Yet many people claim their product caused a positive outcome when, in fact, the data do not support their claim any more than the water example. This brings up something that confuses even seasoned researchers. When you have only one observation it is called a datum. When you use the word data, it refers to multiple observations, so it is always plural.