TY - JOUR
T1 - Multilevel analyses in marketing research
T2 - differentiating analytical outcomes
AU - Wieske, Jan
AU - Lee, Nick J.
AU - Broderick, Amanda J.
AU - Dawson, Jeremy F.
AU - van Dick, Rolf
PY - 2008/9
Y1 - 2008/9
N2 - Marketing scholars are increasingly recognizing the importance of investigating phenomena at multiple levels. However, the analyses methods that are currently dominant within marketing may not be appropriate to dealing with multilevel or nested data structures. We identify the state of contemporary multilevel marketing research, finding that typical empirical approaches within marketing research may be less effective at explicitly taking account of multilevel data structures than those in other organizational disciplines. A Monte Carlo simulation, based on results from a previously published marketing study, demonstrates that different approaches to analysis of the same data can result in very different results (both in terms of power and effect size). The implication is that marketing scholars should be cautious when analyzing multilevel or other grouped data, and we provide a discussion and introduction to the use of hierarchical linear modeling for this purpose.
AB - Marketing scholars are increasingly recognizing the importance of investigating phenomena at multiple levels. However, the analyses methods that are currently dominant within marketing may not be appropriate to dealing with multilevel or nested data structures. We identify the state of contemporary multilevel marketing research, finding that typical empirical approaches within marketing research may be less effective at explicitly taking account of multilevel data structures than those in other organizational disciplines. A Monte Carlo simulation, based on results from a previously published marketing study, demonstrates that different approaches to analysis of the same data can result in very different results (both in terms of power and effect size). The implication is that marketing scholars should be cautious when analyzing multilevel or other grouped data, and we provide a discussion and introduction to the use of hierarchical linear modeling for this purpose.
KW - research
KW - marketing
KW - analytical outcomes
UR - http://www.scopus.com/inward/record.url?scp=52749098360&partnerID=8YFLogxK
UR - https://www.tandfonline.com/doi/abs/10.2753/MTP1069-6679160405
U2 - 10.2753/MTP1069-6679160405
DO - 10.2753/MTP1069-6679160405
M3 - Article
SN - 1069-6679
VL - 16
SP - 321
EP - 340
JO - Journal of Marketing Theory and Practice
JF - Journal of Marketing Theory and Practice
IS - 4
ER -