Edited by Alan Carsrud and Malin Brännback
When investigating entrepreneurial phenomena, scholars are frequently interested in understanding causal relationships. Conducting an experiment provides better insights into causal relationships than many other research strategies (Schwab, 2005). This is because researchers are better able to eliminate alternative explanations among the predictor and criterion variables by randomly assigning individuals to experimental conditions and manipulating the variables. For several reasons (e.g. an experiment may not be feasible), researchers frequently use a nonexperimental research design (Austin et al., 2002; Stone-Romero, 2007). However, nonexperimental research does not benefit from the control of variables possible in experimental research. Because extraneous nuisance variables - variables that influence the variables of interest in a study but are not central to the study - are correlated with the variables of interest, the use of nonexperimental research design makes drawing causal inferences difficult. These nuisance variables offer alternative explanations for the relationship among the variables of interest - the predictor and criterion variables. To limit such alterative explanations, researchers often attempt to measure extraneous nuisance variables presumed to be associated the variables of interest in the relationship investigated. These extraneous nuisance variables are, then, controlled for in correlational research, such as multiple regression analysis, to rule out alternative explanations or to reduce error variance (Becker, 2005; Schwab, 2005).
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