Assessing the importance of housing, accessibility, and transportation characteristics on stated neighbourhood preference

References

• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended Two-step approach. Psychological Bulletin, 103(3), 411–423. doi: 10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Bagley, M. N., & Mokhtarian, P. L. (2002). The impact of residential neighborhood type on travel behavior: A structural equations modeling approach. The Annals of Regional Science, 36, 279–297. doi: 10.1007/s001680200083
   Web of Science ®Google Scholar
• Bandalos, D. L. (2014). Relative performance of categorical diagonally weighted least squares and robust maximum likelihood estimation. Structural Equation Modeling: A Multidisciplinary Journal, 21(1), 102–116. doi: 10.1080/10705511.2014.859510
   Web of Science ®Google Scholar
• Bohte, W., Maat, K., & van Wee, B. (2009). Measuring attitudes in research on residential self-selection and travel behaviour: A review of theories and empirical research. Transport Reviews, 29(3), 325–357. doi: 10.1080/01441640902808441
   Web of Science ®Google Scholar
• Currans, K. M., Gehrke, S. R., & Clifton, K. J. (2015). The use of images in transportation surveys: Testing respondents perceptions of housing, transportation and built environment chsaracteristics. Paper presented at the annual meeting of the transportation research board, Washington, DC.
   Google Scholar
• de Abreu e Silva, J. (2014). Spatial self-selection in land-Use-travel behavior interactions: Accounting simultaneously for attitudes and scioeconomic characteristics. Journal of Transport and Land Use, 7(2), 63–84. doi: 10.5198/jtlu.v7i2.696
   Web of Science ®Google Scholar
• De Vos, J., Derudder, B., Van Acker, V., & Witlox, F. (2012). Reducing car use: Changing attitudes or relocating? The influence of residential dissonance on travel behavior. Journal of Transport Geography, 22, 1–9. doi: 10.1016/j.jtrangeo.2011.11.005
   Web of Science ®Google Scholar
• de Winter, J. C. F., & Dodou, D. (2012). Factor recorvery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics, 39(4), 695–710. doi: 10.1080/02664763.2011.610445
   Web of Science ®Google Scholar
• Ford, J. K., MacCallum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied psychology: A critical review and analysis. Personnel Psychology, 39, 291–314. doi: 10.1111/j.1744-6570.1986.tb00583.x
   Web of Science ®Google Scholar
• Frank, L. F., Saelens, B. E., Powell, K. E., & Chapman, J. (2007). Stepping towards causation: Do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Social Science & Medicine, 65, 1898–1914. doi: 10.1016/j.socscimed.2007.05.053
   PubMed Web of Science ®Google Scholar
• Golob, T. F. (2003). Structural equation modeling for travel behavior research. Transportation Research Part B: Methodological, 37, 1–25. doi: 10.1016/S0191-2615(01)00046-7
   Web of Science ®Google Scholar
• Google, Inc. (2011–2014). Google maps street view. Retrieved from https://www.google.com/maps
   Google Scholar
• Handy, S., Mokhtarian, P., Buehler, T., & Cao, X. (2004). Residential location choice and travel behavior: Implications for air quality ( Research Report). Davis, CA: University of California, Institute of Transportation Studies.
   Google Scholar
• Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191–205. doi: 10.1177/1094428104263675
   Web of Science ®Google Scholar
• Henson, R. K., & Roberts, K. J. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. doi: 10.1177/0013164405282485
   Web of Science ®Google Scholar
• Hoshino, T. (2013). Estimation of the preference heterogeneity within stated choice data using semiparametric varying-coefficient methods. Empirical Economics, 45(3), 1129–1148. doi: 10.1007/s00181-012-0646-5
   Web of Science ®Google Scholar
• Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. doi: 10.1080/10705519909540118
   Web of Science ®Google Scholar
• Hunt, J. D. (2001). Stated preference analysis of sensitivities to elements of transportation and urban form. Transportation Research Record: Journal of the Transportation Research Board, 1780(1), 76–86. doi: 10.3141/1780-09
   Google Scholar
• Jansen, S., Boumeester, H., Coolen, H., Goetgeluk, R., & Molin, E. (2009). The impact of including images in a conjoint measurement task: Evidence from two small-scale studies. Journal of Housing and the Built Environment, 24(3), 271–297. doi: 10.1007/s10901-009-9149-x
   Web of Science ®Google Scholar
• Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. doi: 10.1177/001316446002000116
   Web of Science ®Google Scholar
• Khattak, A. J., & Rodriguez, D. (2005). Travel behavior in Neo-traditional neighobrhood developments: A case study in USA. Transportation Research Part A, 39, 481–500.
   Google Scholar
• Liao, F. H., Farber, S., & Ewing, R. (2015). Compact development and preference heterogeneity in residential location choice behaviour: A latent class analysis. Urban Studies, 52(2), 314–337. doi: 10.1177/0042098014527138
   Web of Science ®Google Scholar
• Lund, H. (2006). Reasons for living in a transit-oriented development, and associated transit use. Journal of the American Planning Association, 72(3), 357–366. doi: 10.1080/01944360608976757
   Web of Science ®Google Scholar
• Maruyama, G. M. (1998). Basics of structural equation modeling. Thousand Oaks, CA: Sage Publications.
   Google Scholar
• Molin, E., & Timmermans, H. (2003). Testing hierarchical information integration theory: The causal structure of household residential satisfaction. Environment and Planning A, 35(1), 43–58. doi: 10.1068/a3416
   Web of Science ®Google Scholar
• Morrow-Jones, H. A., Irwin, E. G., & Roe, B. (2004). Consumer preference for neotraditional neighborhood characteristics. Housing Policy Debate, 15(1), 171–202. doi: 10.1080/10511482.2004.9521498
   Web of Science ®Google Scholar
• Olaru, D., Smith, B., & Taplin, J. (2011). Residential location and transit-oriented development in a new rail corridor. Transportation Research Part A, 45, 219–237.
   Google Scholar
• R Core Team. (2014). R: A language and environment for statistical computing. Vienna. Retrieved from http://www.R-project.org
   Google Scholar
• Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. doi: 10.18637/jss.v048.i02
   Web of Science ®Google Scholar
• Schirmer, P. M., van Eggermon, M. A. B., & Axhausen, K. A. (2014). The role of location in residential location choice models: A review of literature. Journal of Transport and Land Use, 7(2), 3–21. doi: 10.5198/jtlu.v7i2.740
   Web of Science ®Google Scholar
• Schwanen, T., & Mokhtarian, P. L. (2004). The extent and determinants of dissonance between actual and preferred residential neighborhood type. Environment and Planning B: Planning and Design, 31, 759–784. doi: 10.1068/b3039
   Web of Science ®Google Scholar
• Senior, M. L., Webster, C. J., & Blank, N. E. (2006). Residential relocation and sustainable urban form: Statistical analyses of owner-occupiers’ preferences. International Planning Studies, 11(1), 41–57. doi: 10.1080/13563470600935024
   Google Scholar
• Walker, J., & Li, J. (2007). Latent lifestyle preferences and household location decisions. Journal of Geographical Systems, 9, 77–101. doi: 10.1007/s10109-006-0030-0
   Web of Science ®Google Scholar