A note on bias in project management research
Project management research relies heavily on empirical studies – that is, studies that are based on observation of reality. This is necessary because projects are coordinated activities involving real-world entities: people, teams and organisations. A project management researcher can theorise all he or she likes, but the ultimate test of any theory is, “do the hypotheses agree with the data?” In this, project management is no different from physics: to be accepted as valid, any theory must agree with reality. In physics (or any of the natural sciences), however, experiments can be carried out in controlled conditions that ensure objectivity and the elimination of any extraneous effects or biases. This isn’t the case in project management (or for that matter any of the social sciences). Since people are the primary subjects of study in the latter, subjectivity and bias are inevitable. This post delves into the latter point with an emphasis on project management research.
From my reading of several project management research papers, most empirical studies in project management proceed roughly as follows:
- Formulate a hypotheses based on observation and / or existing research.
- Design a survey based on the hypotheses.
- Gather survey data.
- Accept or reject the hypotheses based on statistical analysis of the data.
- Discuss and generalise.
Survey data plays a crucial role in empirical project management studies. This pleads the question: Do researchers account for bias in survey responses? Before proceeding, I’d like to clarify the question with with an example. Assume I’m a project manager who receives a research survey asking questions about my experience and the kinds of projects I have managed. What’s to stop me from inflating my experience and exaggerating the projects I have run? Answer: Nothing! Now, assuming that a small (or, possibly, not so small) percentage of project managers targeted by research surveys stretch the truth for whatever reason, the researcher is going to end up with data that is at least partly garbage. Hence the italicised question that I posed at the start of this paragraph.
The tendency of people to describe themselves in a positive light referred to as social desirability bias. It is impossible to guard against, even if the researcher assures respondents of confidentiality and anonymity in analysis and reporting. Clearly this is more of a problem when used for testing within an organisation: respondents may fear reprisals for being truthful. In this connection William Whyte made the following comment in his book The Organization Man, “When an individual is commanded by an organisation to reveal his innermost feelings, he has a duty to himself to give answers that serve his self-interest rather than that of The Organization.” Notwithstanding this, problems remains even with external surveys. The bias is lessened by anonymity, but doesn’t completely disappear. It seems logical that people will be more relaxed with external surveys (in which they have no direct stake), more so if they are anonymous. However, one cannot be completely certain that responses are bias-free.
Of course, researchers are aware of this problem, and have devised techniques to deal with it. The following methods are commonly used to reduce social desirability bias
- The use of scales, such as the Marlowe-Crowne social desirability scale, to determine susceptibility of respondents to social desirability bias. These scales are based on responses to questions that represent behaviours which are socially deemed as desirable, but at the same time very unlikely. It’s a bit hard to explain; the best way to understand the concept is to try this quiz. A recognised limitation of do not distinguish between genuine differences and bias. Many researchers have questioned the utility of such scales on other grounds as well- see this paper, for example.
- The use of forced choice responses – where respondents are required to choose between different scenarios rather than assigning a numerical (or qualitative) rating to a specific statement. In this case, survey design is very important as the choices presented need to be well-balanced and appropriately worded. However, even with due attention to design, there are well-known problems with forced choice response surveys (see this paper abstract, for example).
It appears that social desirability bias is hard to eliminate, though with due care it can be reduced. As far as I can tell (from my limited reading of project management research), most researchers count on guaranteed anonymity of survey responses as being enough to control this bias. Is this good enough? May be it is, may be not: academics and others are invited to comment.