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Causal - Comparative Research

“If we want to reduce the amount of changes made at the end of our projects, then we need to find out the causes to so many changes, but that means we should do causal-comparative research.”

Nowadays they tell us that the Boston Massacre wasn’t really what it was claimed to be. What was the cause? There are times that we might feel discovering the cause of project problems might be just as unpopular as digging around in the Boston Massacre. But if you must, then do it right.

One of the most common research methods used in Integrated PM is the Causal-Comparative research method. It’s used to investigate possible cause-and-effect relationships by observing some existing consequence (effect) and searching back through the data for plausible causal factors. This contrasts with the experimental method which collects its data under controlled conditions in the present.

Examples:

  1. To identify factors characterizing persons having either high or low approval rates, using data from past project records.
  2. To determine the attributes of effective project sponsors as defined, for example, by their realized project value. Portfolio and program records over the past five years are then examined, comparing these data to the number of innovative projects or several other factors.
  3. To look for patterns of behavior and achievement associated with project manager experience differences, using descriptive data on project behavior and project value achievement.

Principal Characteristics: Causal-comparative research is "ex post facto" in nature, which means the data are collected after all the events of interest have occurred. The investigator then takes one or more effects (dependent variables) and examines the data by going back through time, seeking out causes, relationships, and their meanings.

Strengths: The causal-comparative method is appropriate in many circumstances where the more powerful experimental method is not possible. It is used when it is not always possible to select, control, and manipulate the facts necessary to study cause-and-effect relations directly. It also can be used when the control of all variations except a single independent variable may be highly unrealistic and artificial, preventing the normal interaction with other influential variables. Of course, with most project conditions, it is used when laboratory controls for many research purposes would be impractical, costly, or ethically questionable.

Note: The experimental method involves both an experimental and a control group. Some treatment "A" is given the experimental group, and the result "B" is observed. The control group is not exposed to "A" and their condition is compared to the experimental group to see what effects "A" might have had in producing "B." In the causal-comparative method, the investigator reverses this process, observing a result "B" which already exists and searches back through several possible causes ("A" type of events) that are related to "B."

  1. It yields useful Integrated PM information concerning the nature of phenomena: what goes with what, under what conditions, in what sequences and patterns, and the like.
  2. Improvements in techniques, statistical methods, and designs with partial control features, in recent years involving Integrated PM, have made these studies more defensible.

Weaknesses: The main weakness of any ex post facto design is the lack of control over independent variables. Within the limits of selection, the investigator must take the facts as they are found with no opportunity to arrange the conditions or manipulate the variables that influenced the facts in the first place. To reach sound conclusions, the investigator must consider all the other possible reasons or plausible rival hypotheses which might account for the results obtained. To the extent that the conclusions can be successfully justified against these other alternatives puts the investigator in a position of relative strength. The difficulty in being certain that the relevant causative factor is included among the many factors under study.

  1. The complication that no single factor is the cause of an outcome but some combination and interaction of factors go together under certain conditions to yield a given outcome. A phenomenon may result not only from multiple causes but also from one cause in one instance and from another cause in another instance.
  2. When a relationship between two variables is discovered, determining which is the cause and which the effect may be difficult.
  3. The fact that two or more factors are related does not necessarily imply a cause-and-¬effect relationship. They all simply may be related to an additional factor not recognized or observed. Classifying subjects into dichotomous groups (e.g., "Achievers" and "Non-achievers") for comparison is fraught with problems, since categories like these are vague, variable, and transitory. Such investigations often do not yield useful findings.
  4. Comparative studies in natural situations do not allow controlled selection of subjects. Locating existing groups of subjects who are similar in all respects except for their exposure to one variable is extremely difficult.

Steps:

  1. Define the problem.
  2. Survey the literature. State the hypotheses. List the assumptions upon which the hypotheses and procedures will be based.
  3. Design the approach:
    • Select appropriate subjects and source materials.
    • Select or construct techniques for collecting the data.
    • Establish categories for classifying data that are unambiguous, appropriate for the study, and capable of bringing out significant likenesses or relationships.
  4. Validate the data-gathering techniques.
  5. Describe, analyze, and interpret the findings in clear, precise terms.
Correlational Research
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Wednesday, 22 November 2017
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