Handbook of Research Methods and Applications in Entrepreneurship and Small Business

Handbook of Research Methods and Applications in Entrepreneurship and Small Business

Handbooks of Research Methods and Applications series

Edited by Alan Carsrud and Malin Brännback

This thought provoking book builds on existing research traditions that make small business, entrepreneurship and family business a resource rich arena for study. It steps back to ask fundamental questions that every researcher should consider prior to engaging in data collection. It focuses on topics that have traditionally frustrated researchers including experimental methods in small business research, scale development, control variables and language issues in cross cultural research.

Chapter 7: Control variables: use, misuse and recommended use

Leon Schjoedt and Barbara Bird

Subjects: business and management, entrepreneurship, family business, research methods in business and management, research methods, research methods in business and management

Extract

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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