Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-19T00:27:44.532Z Has data issue: false hasContentIssue false
  • English
  • Français

Impact of individual and organizational factors on job satisfaction: A comparison of multilevel models and multiple regression models using different data arrangements

Published online by Cambridge University Press:  07 October 2013

Jun Yi Hsieh
Jun Yi Hsieh*
Affiliation:
Department of Public Affairs, University of Taipei, Taipei, Taiwan
*
Corresponding author: jh04e@utaipei.edu.tw
Get access Rights & Permissions [Opens in a new window]

Abstract

Typically most studies of individual employees perceptions of the work place adopt multiple regression models (ordinary least squares [OLS]) which ignore inherent clustering in their data. However, such an approach does not supply unbiased and accurate answers to research questions. This study intends to simulate three data alternatives – weighted, disaggregated (individual level), and aggregated (organizational level) using the OLS and multilevel models to compare the results of different research designs. To answer the research questions, the current study investigates the impact of individual and organizational factors on job satisfaction, using a 2000 USA National Partnership for Reinventing Government survey. This study presents the methodological misuse and measurement errors of the previous research and presents guidelines for future research.

Keywords

multiple regression modelmultilevel modelaggregationjob satisfactionpublic employee
Type
Methodology
Information
Journal of Management & Organization , Volume 19 , Issue 1 , January 2013 , pp. 44 - 59
Copyright
Copyright © Cambridge University Press and Australian and New Zealand Academy of Management 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Broom, C., Sharon, C., Jennings, E. T., Newcomer, K. (2002). Meeting the challenges of performance-oriented government. In K. Newcomer, E. T., Jr., Jennings, C. Broom, & A. Lomax (Eds.), Meeting the challenges of performance-oriented government (pp. 1–12). Washington, DC: American Society for Public Administration.Google Scholar
Chatterjee, S., Hadi, A. S. (2006). Regression analysis by example. Hoboken, NJ: John Wiley & Sons.Google Scholar
Churchill, G., Ford, N. M., Walker, O. C. Jr. (1974, August). Measuring the job satisfaction of industrial salesmen. Journal of Marketing Research, 11, 254–260.CrossRefGoogle Scholar
de Vaus, D. (2004). Research design in social research. Thousand Oaks, CA: Sage.Google Scholar
Draper, D. (1995). Inference and hierarchical modeling in the social sciences. Journal of Educational Statistics, 20, 115–148.Google Scholar
Groves, R. M., Fowler, F. J. Jr., Couper, M. P., Lepkowski, J. M., Singer, E., Tourangeau, R. (2009). Survey methodology (2nd ed.). Hobooken, NJ: John Wiley & Sons.Google Scholar
Gujarati, D. N. (2003). Basic econometrics (4th ed.). New York, NY: The McGraw-Hill Companies.Google Scholar
Haucka, K., Street, A. (2006). Performance assessment in the context of multiple objectives: A multivariate multilevel analysis. Journal of Health Economics, 25, 1029–1048.Google Scholar
Heck, R. H., Thomas, S. L. (2009). An introduction to multilevel modeling techniques (2nd ed.). New York, NY: Routledge.Google Scholar
Heinrich, C. J., Lynn, L. E. (2000). Means and ends: A comparative study of empirical methods for investigating governance and performance. Journal of Public Administration Research and Theory, 11(1), 109–138.CrossRefGoogle Scholar
Hoegl, M., Gemuenden, H. G. (2001). Teamwork quality and the success of innovative projects: A theoretical concept and empirical evidence. Organization Science, 12(4), 435–449.CrossRefGoogle Scholar
Klein, K. J., Dansereau, F., Hall, R. J. (1994). Level issues in theory development, data collection, and analysis. Academy of Management Review, 19, 195–229.Google Scholar
Hofmann, D. A., Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623–641.Google Scholar
Kozlowski, S. W., Klein, K. J. (2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent process. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 3–90). San Francisco, CA: Jossey-Bass.Google Scholar
Kreft, I., De Leeus, J. (1998). Introducing multilevel modeling. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
Lewis, G. B., Nice, D. (1994). Race, sex and occupational segregation in state and local governments. American Review of Public Administration, 24, 393–410.Google Scholar
McCoach, D. B., Black, A. C. (2008). Evaluation of model fit and adequacy. In A. A. O'Connell & D. Betsy McCoach (Eds.), Multilevel modeling of educational data (pp. 245–272) Charlotte, NC: Information Age Publishing.Google Scholar
Meade, A. W., Eby, L. T. (2007). Using indices of group agreement in multilevel construct validation. Organizational Research Methods, 10(1), 75–96.CrossRefGoogle Scholar
Meier, K. J., O'Toole, L. J., Nicholson-Crotty, S. (2004). Multilevel governance and organizational performance: Investigating the political-bureaucratic labyrinth. Journal of Policy Analysis and Management, 23(1), 31–47.Google Scholar
Pearce, J. L., Perry, J. L. (1983). Federal merit pay: A longitudinal analysis. Public Administration Review, 43(4), 315–325.CrossRefGoogle Scholar
Perry, J. L., Wise, L. R. (1990). The motivation bases of public service. Public Administration Review, 50(3), 367–373.Google Scholar
Ployhart, R. E., Weekley, J. A., Barughan, K. (2006). The structure and function of human capital emergency: A multilevel examination of the attraction-selection-attrition model. Academy of Management Journal, 49(4), 661–677.Google Scholar
Provan, K. G., Milward, H. B. (2001). Do networks really work? A framework for evaluating public sector organizational networks. Public Administration Review, 61(4), 400–409.CrossRefGoogle Scholar
Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.Google Scholar
Rainey, H. G. (2003). Understanding and managing public organization (3rd ed.). San Francisco, CA: John Wiley & Sons.Google Scholar
Raudenbush, S. W., Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. Sociological Review, 15, 351–357.Google Scholar
Schneider, M. (2007). Do attributes of innovative administrative practices influence their adoption? An exploratory study of U.S. local government. Public Performance & Management Review, 30(4), 598–622.Google Scholar
Snijders, T. A. B., Bosker, R. J. (1999). Multilevel analysis. Thousand Oaks, CA: Sage.Google Scholar
Wagenheim, G. D., Reurink, J. H. (1991). Customer service in public administration. Public Administration Review, 51(3), 263–270.Google Scholar
Wolfe, R. A. (1994). Organizational innovation: Review, critique and suggested research directions. Journal of Management Studies, 31(3), 405–431.Google Scholar
Wooldridge, J. M. (2006). Introductory econometrics: A modern approach (3rd ed.). Mason, OH: Thomson Higher Education.Google Scholar
Yukl, G. (2001). Leadership in organizations (5th ed.). Prentice Hall, NJ: Upper Saddle River.Google Scholar
Zaccarin, S., Rivellini, G. (2002). Multilevel analysis in social research: an application of a cross-classified model. Statistical Methods & Applications, 11, 95–108.CrossRefGoogle Scholar