Dynamic Multilevel Covariance Structure Models for Analyzing Rolling Cross-Sectional Tracking Surveys

40 Pages Posted: 22 Feb 2018

See all articles by Joonwook Park

Joonwook Park

University of Kentucky - Marketing and Supply Chain

William R Dillon

Southern Methodist University (SMU) - Marketing Department

Date Written: Fall 2017

Abstract

Appendix is available at: https://ssrn.com/abstract=3127752

Modeling customer satisfaction tracking data presents some unique challenges. Customer satisfaction tracking data are neither strictly cross-sectional nor strictly longitudinal in a panel sense. We refer to such data as nested repeated (rolling) cross-sectional, reflecting the fact that respondents are nested within brands and that the data are composed of a series of repeated cross-sections. In this study, we develop a new methodology that extends multilevel modeling techniques by considering both temporal variation and individual-level heterogeneity simultaneously in the presence of nested repeated cross-sectional samples. Our modeling approach essentially uses a Dynamic Linear Model (DLM) form and a multilevel structural equation form to “marry” the time series and cross-sectional sources of variation. Our empirical analysis explores the long-term effects of overall customer satisfaction and feature-level satisfaction on brand sales and the salutary benefits, if any, of controlling for individual-level response heterogeneity.

Keywords: Satisfaction Tracking, Performance Metrics, Dynamic Linear Model, Hierarchical Time-Series Cross-Sectional Data

Suggested Citation

Park, Joonwook and Dillon, William R, Dynamic Multilevel Covariance Structure Models for Analyzing Rolling Cross-Sectional Tracking Surveys (Fall 2017). SMU Cox School of Business Research Paper No. 18-15. Available at SSRN: https://ssrn.com/abstract=3127710 or http://dx.doi.org/10.2139/ssrn.3127710

Joonwook Park

University of Kentucky - Marketing and Supply Chain ( email )

United States

William R Dillon (Contact Author)

Southern Methodist University (SMU) - Marketing Department

United States

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