Marketing i Zarządzanie

Previously: Zeszyty Naukowe Uniwersytetu Szczecińskiego. Problemy Zarządzania, Finansów i Marketingu


ISSN: 2450-775X     eISSN: 2543-5574    OAI    DOI: 10.18276/miz.2018.54-05
CC BY-SA   Open Access 

Issue archive / nr 4 (54) 2018
Dynamic Structural Equation Models in Momentary Assessment in Consumer Research

Authors: Adam Sagan
Cracow University of Economics
Keywords: experience sampling method consumer momentary assessment dynamic structural equation models
Whole issue publication date:2018
Page range:13 (61-73)
Klasyfikacja JEL: M31
Cited-by (Crossref) ?:

Abstract

The aim of the paper is to provide methodological framework to model intensive longitudinal data (ILD). The specific types of such data are consumer moods and emotional feelings that constitute the satisfaction states of the consumer. The research process of ILD involves ecological momentary assessment and experience sampling methods that are characterized by higher ecological validity. In the paper the special type of structural models, namely dynamic structural equation model (DSEM) are developed for proper analysis of multilevel longitudinal data. The models are built on the basis of consumer mood scale. The data were gathered from a convenient sample of 33 respondents and a systematic sample of time moments that provide a total of 640 observations. The results show the insignificant role of the socio-demographic characteristic of the respondents (gender) in explanation of very flexible psychological states.
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Bibliography

1.Asparouhov, T., Hamaker, E.L., Muthen, B.O. (2018). Dynamic Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 25 (3), 359–388.
2.Asparouhov, T., Muthen, B.O. (2018). Comparison of Models for the Analysis of Intensive Longitudinal Data. Retrieved from: http://www.statmodel.com/download/RDSEM.pdf.
3.Bolger, N., Laurenceau, J. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. New York: Guilford.
4.Hektner, J.M., Schmidt, J.A., Csikszentmihalyi, M. (2012). Experience Sampling Method: Measuring the Quality of Everyday Life. Thousand Oaks, CA: Sage.
5.Muthen, B.O., Muthen, L.O. (2017). Mplus. Statistical Analysis with Latent Variables. User Guide. Los Angeles: Muthen & Muthen.
6.Nezlek, J.B. (2012). Multilevel Modeling Analyses of Diary-Style Data. In: M.R. Mehl, T.S. Conner, Handbook of Research Methods for Studying Daily Life. New York: Guilford Press.
7.Stone, A., Shiffman, S. (1994). Ecological Momentary Assessment (EMA) in Behavioral Medicine. Annals of Behavioral Medicine, 16 (3), 199–202.
8.Tan, X., Shiyko, MP., Li, R., Li, Y., Dierker, L. (2010). Intensive Longitudinal Data and Model with Varying Effects (Technical Report No 10–106). University Park, PA: The Methodology Center, The Pennsylvania State University.
9.Trull, T.J., Ebner-Priemer, U. W. (2009). Using Experience Sampling Methods/Ecological Momentary Assessment (ESM/EMA) in Clinical Assessment and Clinical Research: Introduction to the Special Section. Psychological Assessment, 21 (4), 457–462.
10.Zhang, Z., Hamaker, E., Nesselroade, J. (2008). Comparisons of Four Methods for Estimating a Dynamic Factor Model. Structural Equation Modeling, 15, 377–402.