The Electronic Journal of Business Research Methods provides perspectives on topics relevant to research in the field of business and management
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Journal Issue
Volume 17 Issue 3 / Sep 2019  pp102‑191

Editor: Ann Brown

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A Detailed Guide on Converting Qualitative Data into Quantitative Entrepreneurial Skills Survey Instrument  pp102‑117

Anastacia Mamabolo, Kerrin Myres

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Using the TACT Framework to Learn the Principles of Rigour in Qualitative Research  pp118‑129

Ben K. Daniel

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Addressing The Challenge of Building Research Capabilities in Business Management Undergraduate Students  pp130‑142

Martin Rich, Ann Brown, Aneesh Banerjee

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An Illustration of a Deductive Pattern Matching Procedure in Qualitative Leadership Research  pp143‑154

Noel Pearse

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Augmenting Social Media Research with Q Methodology: Some Guiding Principles  pp155‑164

Charmaine du Plessis

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Sequencing Effects in the Analysis of Complex Experiments in Business Research: Mechanisms, Biases, and Recommendations  pp165‑178

Peter Kotzian

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Experiments in business research became more complex over time, yielding complex sequences of stimuli and measurements. This raises the issue of sequence effects, where effects are found only in specific sequences of the experiment. One case in point is factorial surveys. Here, presenting the stimulus is followed by asking subjects to evaluate several vignettes presented in a certain sequence. The researcher is interested in the effect of the stimulus on responses to vignettes with certain features. As sequence and stimulus can be made uncorrelated by construction, holding the sequence constant or excluding the sequence from the analysis seems to be justified when researchers are only interested in effects of vignette features or the stimulus. In both cases, even if the sequence is relevant for the dependent variable, correlation between sequence and stimulus, the necessary condition for an omitted variable bias, is absent. The effect estimated for the stimulus should thus be unbiased. We show that even in the case where stimulus and sequence are uncorrelated or the sequence is held constant, an omitted variable bias occurs when the effect of the stimulus in a vignette is in its magnitude dependent on the sequence in which the vignettes were presented. Such an effect would be modeled by including a sequence‑stimulus‑interaction term and the omitted variable is this interaction term, which is, by construction, always correlated with each of the constitutive variables. A simulation is presented to illustrate the problem. Implications for experimental research are discussed. 


Keywords: Experimental Design; Factorial Surveys; Order-effects; Omitted-Variable Bias JEL Codes: C21; C91


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Business Research Methodologies and the need for Economies of Scale in the Business Research Process: Harnessing the Innovation Opportunities of Novel Technologies and Technological Change  pp179‑190

Chris William Callaghan

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EJBRM Editorial for Volume 17 Issue 3 2019  pp191‑191

Ann Brown

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