Quantitative methods in tourism: A handbook

Quantitative Methods in Tourism: A Handbook

Volumn , Issue , 2011, Pages 1-225

  Klobas, Jane ^b  

^a BOCCONI UNIVERSITY   (Italy)

^b UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84948170154     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (321)

1. Babbie, E. (2010) The Practice of Social Research (12th edn). Belmont, CA: Wadsworth.
2. Batini, C. and Scannapieco, M. (2006) Data Quality: Concepts, Methods and Techniques. Berlin: Springer.
3. Campbell, E.G., Clarridge, B.R., Gokhale, M., Birenbaum, L., Hilgartner, S., Holtzen, N.A. and Blumenthal, D. (2002) Data withholding in academic genetics: Evidence from a national survey. Journal of the American Medical Association 287 (4), 473-480.
4. Chung, T. and Law, R. (2003) Developing a performance indicator for hotel websites. International Journal of Hospitality Management 22 (1), 119-125.
5. Creswell, J.W. (2003) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd edn). Thousand Oaks, CA: Sage.
6. Cser, K. and Ohuchi, A. (2008) World practices of hotel classification systems. Asia Pacific Journal of Tourism Research 13, 379- 398.
7. Dey, B. and Sarma, M.K. (2010) Information source usage among motive-based segments of travelers to newly emerging tourist destinations. Tourism Management 31, 341-344.
8. Dillman, D.A. (2007) Mail and Internet Surveys: The Tailored Design Method (2nd edn). Hoboken, NJ: John Wiley.
9. Erl, T. (2005) Service-Oriented Architecture: Concepts, Technology, and Design. Upper Saddle River, NJ: Prentice Hall.
10. Eurostat (2000) Community Methodology on Tourism Statistics. Luxembourg: Office for the Official Publications of the European Communities.
11. Eurostat (2002) Methodology of Short-term Business Statistics: Interpretation and Guidelines. Luxembourg: Office for the Official Publications of the European Communities.
12. Eurostat (2005a) Quality Measures for Economic Indicators. Luxembourg: Office for the Official Publications of the European Communities.
13. Eurostat (2005b) Standard Quality Indicators. Luxembourg: Office for the Official Publications of the European Communities.
14. Frank, A.U., Grum, E. and Vasseur, B. (2004) Procedure to select the best dataset for a task. In M.J. Egenhofer, C. Freksa and H.J. Miller (eds) Geographic Information Science (pp 81-93). Berlin: Springer.
15. Gruber, T.R. (1993) A translation approach to portable ontology specifications. Knowledge Acquisition 5 (2), 199-220.
16. Hair, J.F., Babin, B.J., Money, A.H. and Samouel, P. (2005) Essentials of Business Research Methods. Hoboken, NJ: John Wiley.
17. Hotelstars (2010) Catalogue of Criteria 2010-2014. Brussels: Hotelstars Union - HOTReC (Hotels, Restaurants & Cafes in Europe). On WWW at http://hotelstars.eu/userfiles/files/en/downloads/Criteria_2010-2014.pdf. Accessed 6/10.
18. Hystad, P.W. and Keller, P.C. (2008) Towards a destination tourism disaster management framework: Long-term lessons from a forest fire disaster. Tourism Management 29 (1), 151-162.
19. IHRA(2004) The Joint WTO & IH&RA Study on Hotel Classification. Geneva: International Hotel & Restaurant Association. On WWW at http://www.ih-ra. com/marketplace/WTO_IHRA_Hotel_classification_study.pdf. Accessed 6/10.
20. Kent, R.A. (2001) Data Construction and Data Analysis for Survey Research. Basingstoke, UK: Palgrave.
21. Laliberte, L., Griinewald, W. and Probst, L. (2004) Data quality: A comparison of IMF's data quality assessment framework (DQAF) and Eurostat's quality definition. Paper presented at the IMF/OECD Workshop Assessing and Improving Statistical Quality, Paris, 5-7 November 2003. On WWW at http://www.imf.org/external/pubs/ft/bop/2003/dataq.pdf. Accessed 6/10.
22. Neuman, W.L. (2006) Social Research Methods: Qualitative and Quantitative Approaches (6th edn). Boston, MA: Pearson.
23. Pan, B. (2010) Online travel surveys and response patterns. Journal of Travel Research 49, 121-135.
24. Phillimore, J. and Goodson, L. (2004) Qualitative Research in Tourism: Ontologies, Epistemologies and Methodologies. London: Routledge.
25. Pipino, L.L., Lee, Y.W. and Wang, R.Y. (2002) Data quality assessment. Communications of the ACM 45 (4), 211-218.
26. Shoval, N. and Isaacson, M. (2010) Tourist Mobility and Advanced Tracking Technologies. New York: Routledge.
27. Smith, S.L.J. (1988) Tourism as an industry: Debates and concepts. In D. Ioannides and K.G. Debbage (eds) The Economic Geography of the Tourist Industry: A Supply-side Analysis (pp 31-52). London: Routledge.
28. Sonnenwald, D. (2007) Scientific collaboration. Annual Review of Information Science and Technology 41, 643-681.
29. UNSD (2010) International Recommendations for Tourism Statistics 2008. New York: United Nations, Department of Economic and Social Affairs (Statistics Division). On WWW at http://unstats.un.org/unsd/trade/IRTS/IRTS%202008% 20unedited.pdf. Accessed 6/10.
30. UNWTO (2000) Basic References on Tourism Statistics. Madrid: World Tourism Organization.
31. Vannan, E. (2001) Quality data - an improbable dream? Educause Quarterly 24 (1), 56-58.
32. Veal, A.J. (2006) Research Methods for Leisure and Tourism: A Practical Guide (3rd edn). Harlow, UK: Financial Times - Prentice Hall/Pearson Education.
33. Veal, A.J. Wikipedia (2010) Simpson's paradox. On WWW at http://en.wikipedia.org/w/index.php?title=Simpson%27s_paradox. Accessed 12/10.
34. Woodside, A.G. and Dubelaar, C. (2002) A general theory of tourism consumption systems: A conceptual framework and an empirical exploration. Journal of Travel Research 41 (2), 120-132.
35. Yin, R.K. (1994) Case Study Research, Design and Methods (2nd edn). London: Sage.
36. Zins, C. (2007) Conceptual approaches for defining data, information, and knowledge. Journal of the American Society for Information Science and Technology58 (4), 479-493.
37. Antonioli Corigliano, M. and Baggio, R. (2006) On the significance of tourism website evaluations. In M. Hitz, M. Sigala and J. Murphy (eds) Information and Communication Technologies in Tourism 2006 - Proceedings of the International Conference in Lausanne, Switzerland (pp 320-331). Wien: Springer.
38. Brander, L.M., Van Beukering, P. and Cesar, H.S.J. (2007) The recreational value of coral reefs: A meta-analysis. Ecological Economics 63 (1), 209-218.
39. Brons, M., Pels, E. Nijkamp, P. and Rietveld P. (2002) Price elasticities of demand for passenger air travel: A meta-analysis. Journal of Air Transport Management 8 (3), 165-175.
40. Cochran, W.G. (1937) Problems arising in the analysis of a series of similar experiments. Journal of the Royal Statistical Society, 4 (Suppl), 102-118.
41. Cochran, W.G. (1977) Sampling Techniques (3rd edn). New York: John Wiley.
42. Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences (2nd edn). Hillsdale, NJ: Lawrence Erlbaum Associates.
43. Crouch, G.I. (1995) A meta-analysis of tourism demand. Annals of Tourism Research 22 (1), 103-118.
44. Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge: Cambridge University Press.
45. Duval, S. and Tweedie, R. (2000) Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56 (2), 455-463.
46. Efron, B. (1979) Bootstrap methods: Another look at the jackknife. Annals of Statistics 7, 1-26.
47. Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap. New York: Chapman & Hall.
48. English, D.B.K. (2000) A simple procedure for generating confidence intervals in tourist spending profiles and resulting economic impacts. Journal of Regional Analysis and Policy 30, 59- 74.
49. Fisher, R.A. (1935) The Design of Experiments. Edinburgh: Oliver and Boyd.
50. Gehrels, N. (1986) Confidence limits for small numbers of events in astrophysical data. Astrophysical Journal 303, 336-346.
51. Glass, G.V. (1976) Primary, secondary and meta-analysis of research. Educational Researcher 5, 3- 8.
52. Hartung, J., Knapp, G. and Sinha, B.K. (2008) Statistical Meta-Analysis with Applications. Hoboken, NJ: Wiley.
53. Hedges, L.V. and Olkin, I. (1985) Statistical Methods for Meta-Analysis. Orlando, FL: Academic Press.
54. Kanji, G.K. (2006) 100 Statistical Tests (3rd edn). London: Sage.
55. Langley, R. (1971) Practical Statistics. New York: Dover.
56. Lim, C. (1999) A meta-analytic review of international tourism demand. Journal of Travel Research 37 (3), 273-284.
57. Mak, T.K. (2004) Estimating variances for all sample sizes by the bootstrap. Computational Statistics & Data Analysis 46, 459-467.
58. Newcomb, S. (1886) A generalized theory of the combination of observations so as to obtain the best result. American Journal of Mathematics 8 (4), 343-366.
59. Pearson, K. (1904) Report on certain enteric fever inoculation statistics. British Medical Journal 3, 1243-1246.
60. Pol, A.P., Pascual, M.B. and Vazquez, P.C. (2006) Robust estimators and bootstrap confidence intervals applied to tourism spending. Tourism Management 27 (1), 42-50.
61. Rao, C.R. (2004) Combining information from different sources to estimate a common effect and use of multiple measurements for ecological assessment. Environmetrics 15, 415-422.
62. Regener, V.H. (1951) Statistical significance of small samples of cosmic ray counts. Physical Review 84, 161-162.
63. Shao, J. (1999) Mathematical Statistics. Heidelberg: Springer.
64. Sheskin, D.J. (2000) Handbook of Parametric and Nonparametric Statistical Procedures. oca
65. Raton, FL: Chapman & Hall/CRC.
66. Siegel, S. and Castellan, N.J.J. (1988) Nonparametric Statistics for the Behavioral Sciences (2nd edn). New York: McGraw-Hill.
67. Sterne, J.A.C. and Egger, M. (2001) Funnel plots for detecting bias in metaanalysis: Guidelines on choice of axis. Journal of Clinical Epidemiology 54 (10), 1046-1045.
68. Triola, M.F. (2009) Elementary Statistics (11th edn). Boston, MA: Addison Wesley.
69. Zwillinger, D. and Kokoska, S. (2000) CRC Standard Probability and Statistics Tables and Formulae. Boca Raton, FL: Chapman & Hall/CRC.
70. Brown, T.A. (2006) Confirmatory Factor Analysis for Applied Research. New York: The Guilford Press.
71. Cattell, R.B. (1966) The scree test for the number of factors. Multivariate Behavioral Research 1, 245-276.
72. Cronbach, L.J. (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16 (3), 297-334.
73. Cronbach, L.J. and Shavelson, R.J. (2004) My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement 64 (3), 391-418.
74. Cudeck, R. and McCallum, R.C. (2007) Factor Analysis at 100: Historical Developments and Future Directions. Mahwah, NJ: Lawrence Erlbaum Associates.
75. Darlington, R.B. (1968) Multiple regression in psychological research and practice. Psychological Bulletin 69 (3), 161-182.
76. Darlington, R.B. (1997) Factor Analysis. Ithaca, NY: Department of Psychology, Cornell University. On WWW at http://www.psych.cornell.edu/darlington/factor.htm. Accessed 6.10.
77. DeLone, W.H. and McLean, E.R. (1992) Information systems success: The quest for the dependent variable. Information Systems Research 3 (1), 60-95.
78. DeLone, W.H. and McLean, E.R. (2004) Measuring E-commerce success: Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce & Business Media 9 (1), 31-47.
79. Everitt, B.S., Landau, S. and Leese, M. (2001) Cluster Analysis. New York: Arnold.
80. Guadagnoli, E. and Velicer, W.F. (1988) Relation of sample size to the stability of component patterns. Psychological Bulletin 103 (2), 265-275.
81. Guilford, J.P. (1954) Psychometric Methods (2nd edn). New York: McGraw Hill.
82. Guttman, L. (1954) Some necessary conditions for factor analysis. Psychometrika19 (2), 149-161.
83. Guttman, L. (1955) The determinacy of factor score matrices with implications for five other basic problems of common-factor theory. The British Journal of Statistical Psychology 8, 65-81.
84. Haddad, H. and Klobas, J.E. (2002) The relationship between visual abstraction and the effectiveness of a pedagogical character-agent. The First International Joint Conference on Autonomous Agents & Multi-Agent Systems, Bologna, Italy: The Conference. On WWW at http://www.vhml.org/workshops/AAMAS/papers/Haddad.pdf.
85. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006) Multivariate Data Analysis (6th edn). Upper Saddle River, NJ: Prentice Hall.
86. Hair, J., Anderson, R., Tatham, R. and Black, W.R. (2009) Multivariate Data Analysis(7th edn). Upper Saddle River, NJ: Prentice-Hall.
87. Hudson, S. and Ritchie, B. (2002) Understanding the domestic market using cluster analysis: A case study of the marketing efforts of Travel Alberta. Journal of Vacation Marketing 8 (3), 263- 276.
88. Hurley, A.E., Scandura, T.A., Schriesheim, C.A., Brannick, M.T., Seers, A., Vandenberg, R.J. and Williams, L.J. (1997) Exploratory and confirmatory factor analysis: Guidelines, issues, and alternatives. Journal of Organizational Behavior 18, 667-683.
89. Kaiser, H.F. (1960) The application of electronic computers to factor analysis. Educational and Psychological Measurement 20, 141-151.
90. Klobas, J.E. and McGill, T.J. (2010) The role of involvement in learning management system success. Journal of Computing in Higher Education 22 (2), 114-134.
91. Krijnen, W.P., Dijkstra, T.K. and Gill, R.D. (1998) Conditions for factor (in) determinacy in factor analysis. Psychometrika 63 (4), 359-367.
92. Lankford, S.V. and Howard, D.R. (1994) Developing a tourism impact attitude scale. Annals of Tourism Research 21 (1), 121-139.
93. Li, C., Lu, G. and Orti, G. (2008) Optimal data partitioning and a test case for ray-finned fishes (actinopterygii) based on ten nuclear loci. Systematic Biology57 (4), 519-539.
94. Mahalanobis, P.C. (1936) On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India 2 (1), 49-55.
95. McGill, T., Hobbs, V. and Klobas, J. (2003) User-developed applications and information systems success: A test of DeLone and McLean's model. Information Resources Management Journal 16 (1), 24-45.
96. Muthen, L.K. and Muthen, B.O. (2010) Mplus User's Guide (6th edn). Los Angeles, CA: Muthen & Muthen. (On at http://www.statmodel.com/ugex cerpts.shtml).
97. Piccarreta, R. and Billari, F.C. (2007) Clustering work and family trajectories by using a divisive algorithm. Journal of the Royal Statistical Society Series A 170 (4), 1061-1078.
98. Robinson, J.O., Shaver, P.R. and Wrightsman, L.S. (1991) Measures of Personality and Social Psychological Attitudes. New York: Academic Press.
99. Spearman, C. (1904) General intelligence, objectively determined and measured. American Journal of Psychology 15, 201-293.
100. Tabachnick, B.G. and Fidell, L.S. (2006) Using Multivariate Statistics (5th edn). Needham Heights, MA: Allyn & Bacon.
101. Agresti, A. (2002) Categorical Data Analysis (2nd edn). Hoboken, NJ: Wiley.
102. Ajzen, I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179- 211.
103. Anscombe, FJ. (1973) Graphs in statistical analysis. American Statistician 27,17-21.
104. Babbie, E. (2010) The Practice of Social Research (12th edn). Belmont, CA: Wadsworth.
105. Bigano, A., Hamilton, J.M. and Tol, R.S. (2006) The impact of climate on holiday destination choice. Climatic Change 76 (3), 389-406.
106. Bloor, D. (1976) Knowledge and Social Imagery. Chicago: Chicago University Press.
107. Brown, T.A. (2006) Confirmatory Factor Analysis for Applied Research. New York: The Guilford Press.
108. Byrne, B.M. (2009) Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming (2nd rev edn). London: Routledge.
109. Chatterjee, S. and Hadi, A.S. (2006) Regression Analysis by Example (4th edn). Hoboken, NJ: John Wiley & Sons.
110. Cole, S.T. and Illum, S.F. (2006) Examining the mediating role of festival visitors' satisfaction in the relationship between service quality and behavioral intentions. Journal of Vacation Marketing 12 (2), 160-173.
111. Denis, D.D. and Legerski, J. (2006) Causal modeling and the origins of path analysis. Theory and Science 7 (1). On WWW at http://theoryandscience.icaap. org/content/vol7.1/denis.html.
112. Fishbein, M. and Ajzen, I. (1975) Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading: Addison-Wesley.
113. Fishbein, M. and Ajzen, I. (2010) Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press.
114. Fisher, K. (2008) The Wall Street Waltz. Hoboken, NJ: John Wiley & Sons.
115. Gage, N.L. (1978) The Scientific Basis of the Art of Teaching. New York: Teachers College Press.
116. Hair, J.F., Babin, B.J., Money, A.H. and Samouel, P. (2005) Essentials of Business Research Methods. Hoboken, NJ: John Wiley & Sons.
117. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006) Multivariate Data Analysis (6th edn). Upper Saddle River, NJ: Prentice Hall.
118. Hayduck, L.A. (1996) LISREL Issues, Debates and Strategies. Baltimore, MD: Johns Hopkins University Press.
119. Holmes-Smith, P. and Rowe, K.J. (1994) The Development and Use of Congeneric Measurement Models in School Effectiveness Research: Improving the Reliability and Validity of Composite and Latent Variables for Fitting MultiLevel and Structural Equation Models. Paper presented at the International Congress for School Effectiveness and Improvement, Melbourne.
120. Hox, J.J. (2010) Multilevel Analysis: Techniques and Applications (2nd edn). Oxford: Routledge Academic.
121. Joreskog, K.G. and Sorbom, D. (1976) LISREL III: Estimation of Linear Structural Equation Systems by Maximum Likelihood Methods. Chicago, IL: National Educational Resources.
122. Joreskog, K.G. and Sorbom, D. (1993) LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Hillsdale, NJ: Lawrence Erlbaum Associates.
123. Kaplan, D. (2009) Structural Equation Modeling: Foundations and Extensions (2nd edn). Los Angeles: Sage.
124. Knapp, T.R. and Sawilowsky, S.S. (2001) Constructive criticisms of methodological and editorial practices. The Journal of Experimental Education 71 (1), 65- 79.
125. Lise, W. and Tol, R.S.J. (2002) Impact of climate on tourist demand. Climatic Change 55, 429-449.
126. Matthews, R. (2000) Storks deliver babies (p = 0.008). Teaching Statistics 22 (2), 36-38.
127. McGill, T., Hobbs, V. and Klobas, J. (2003) User-developed applications and information systems success: A test of DeLone and McLean's model. Information Resources Management Journal 16 (1), 24.
128. Mieczkowski, Z. (1985) The tourism climatic index: A method of evaluating world climates for tourism. Canadian Geographer 29 (3), 220-233.
129. Miles, J. and Shevlin, M. (2001) Applying Regression & Correlation: A Guide for Students and Researchers. London: Sage.
130. Modis, T. (2007) Sunspots, GDP and the stock market. Technological Forecasting & Social Change 74, 1508-1514.
131. Moroney, M.J. (1967) Facts from Figures (3rd edn). Harmondsworth, UK: Penguin.
132. Shapin, S. and Schaffer, S. (1989) Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life. Princeton, NJ: Princeton University Press.
133. Silvert, W. (2001) Modeling as a discipline. International Journal of General Systems30 (3), 261-282.
134. Snijders, T.A.B. and Bosker, R.J. (1999) Multilevel Analysis: An Introduction to Basic and Advance Multilevel Modeling. London: Sage.
135. Song, H., Witt, S.F. and Li, G. (2009) The Advanced Econometrics of Tourism Demand. New York: Routledge.
136. Thompson, B. (2001) Significance, effect sizes, stepwise methods, and other issues: Strong arguments move the field. Journal of Experimental Education70 (1), 80-93.
137. Thrane, C. (2004) How to present results from logistic regression analysis in hospitality and tourism research. Tourism and Hospitality Research 5 (4), 295-305.
138. Trusty, J., Thompson, B. and Petrocelli, J.V. (2004) Practical guide for reporting effect size in quantitative research in the Journal of Counseling & Development. Journal of Counseling and Development 82, 107-110.
139. Whittingham, M.J., Stephens, P.S., Bradbury, R.B. and Freckleton, R.P. (2006) Why do we still use stepwise modelling in ecology and behaviour? Journal ofAnimal Ecology 75, 1182-1189.
140. Wooldridge, J.M. (2009) Introductory Econometrics (4th edn). Mason, OH: Cengage Learning.
141. Aly, H. and Strazicich, M.C. (2002) Terrorism & Tourism: Is the impact permanent or transitory? Time series evidence from some MENA countries. Economic Research Forum. On WWW at http://www.erf.org.eg/html/btrade1.pdf. Accessed 9.05.
142. Andersen, J.V. and Sornette, D. (2005) A mechanism for pockets of predictability in complex adaptive systems. Europhysics Letters 70 (5), 697-703.
143. Armstrong, J.S. (2001) Combining forecasts. In J.S. Armstrong (ed.) Principles of Forecasting: A Handbook for Researchers and Practitioners (pp 417-439). Boston: Kluwer Academic.
144. Athiyaman, A. and Robertson, R.W. (1992) Time series forecasting techniques: Short-term planning in tourism. International Journal of Contemporary Hospitality Management 4 (4), 8-11.
145. Baggio, R. (2008) Symptoms of complexity in a tourism system. Tourism Analysis 13 (1), 1-20.
146. Baggio, R. and Antonioli Corigliano, M. (2008) A practical forecasting method for a tourism organization. Proceedings of the International Conference: Knowledge as Value Advantage of Tourism Destinations, Malaga, 29-31 October.
147. Bar-Yam, Y. (1997) Dynamics of Complex Systems. Reading, MA: Addison-Wesley.
148. Bates, J.M. and Granger, C.W.J. (1969) The combination of forecasts. Operational Research Quarterly 20, 451-468.
149. Belaire-Franch, J. and Contreras, D. (2002) How to compute the BDS test: A software comparison. Journal of Applied Econometrics 17, 691-699.
150. Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis. Forecasting and Control (rev edn). San Francisco: Holden-Day.
151. Brock, W.A., Dechert, W.D. and Scheinkman, J.A. (1987) A Test for Independence Based on the Correlation Dimension (Working paper). University of Wincon- sin at Madison.
152. Brock, W.A., Dechert, W.D., Scheinkman, J.A. and LeBaron, B. (1996) A test for independence based on the correlation dimension. Econometric Reviews 15 (3), 197-235.
153. Burger, C.J.S.C., Dohnal, M., Kathrada, M. and Law, R. (2001) A practitioners guide to time-series methods for tourism demand forecasting - a case study of Durban, South Africa. Tourism Management 22, 403-409.
154. Chatfield, C. (1996) The Analysis of Time Series (5th edn). New York: Chapman & Hall.
155. Chen, R.J.C., Bloomfield, P. and Cubbage, F.W. (2008) Comparing forecasting models in tourism. Journal of Hospitality and Tourism Research 32, 3-21.
156. Chow, G.C. (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28 (3), 591-605.
157. Cortes-Jimenez, I., Pulina, M., Riera i Prunera, C. and Artis, M. (2009) Tourism and Exports as a Means of Growth (Working Paper 2009/10). Nottingham University, Research Institute of Applied Economics.
158. Davies, B. (2003) The role of quantitative and qualitative research in industrial studies of tourism. International Journal of Tourism Research 5, 97-111.
159. De Gooijer, J.G. and Hyndman, R.J. (2006) 25 years of time series forecasting. International Journal of Forecasting 22, 443-473.
160. De la Fuente, I.M., Martinez, L., Aguirregabiria, J.M. and Veguillas, J. (1998) R/S analysis in strange attractors. Fractals 6, 95-100.
161. Dickey, D.A. and Fuller, W.A. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427-431.
162. Eugenio-Martin, J., Sinclair, M.T. and Yeoman, I. (2005) Quantifying the effects of tourism crises: An application to Scotland. Journal of Travel and Tourism Marketing 19 (2/3), 23-36.
163. Farrell, B.H. and Twining-Ward, L. (2004) Reconceptualizing tourism. Annals of Tourism Research 31 (2), 274-295.
164. Faulkner, B. and Valerio, P. (1995) An integrative approach to tourism demand forecasting. Tourism Management 16 (1), 29-37.
165. Frechtling, D.C. (1996) Practical Tourism Forecasting. Oxford: Butterworth- Heinemann.
166. Frechtling, D.C. (2001) Forecasting Tourism Demand: Methods and Strategies. Oxford: Butterworth-Heinemann.
167. Gourieroux, C. and Monfort, A. (1997) Time Series and Dynamic Models. Cambridge: Cambridge University Press.
168. Gouveia, P.M. and Rodrigues, P.M.M. (2005) Dating and synchronizing tourism growth cycles. Tourism Economics 11 (4), 501-515.
169. Granger, C.W.J. (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424-438.
170. Guizzardi, A. and Mazzocchi, M. (2010) Tourism demand for Italy and the business cycle. Tourism Management 31 (3), 367-377.
171. Hamilton, J.D. (1994) Time Series Analysis. Princeton, NJ: Princeton University Press.
172. Holt, C.C. (1957) Forecasting Seasonals and Trends by Exponentially Weighted Averages (O.N.R. Memorandum 52/1957): Carnegie Institute of Technology. Reprinted with discussion in: International Journal of Forecasting (2004) 20, 5-13.
173. Hurst, H.E. (1951) Long-term storage of reservoirs: An experimental study. Transactions of the American Society of Civil Engineers 116, 770-799.
174. Kantz, H. and Schreiber, T. (1997) Nonlinear Time Series Analysis. Cambridge: Cambridge University Press.
175. Kaplan, M. and Celik, T. (2008) The impact of tourism on economic performance: The case of Turkey. International Journal of Applied Economics and Finance 2 (1), 13-18.
176. Khan, H. (2005) Tourism and trade: Cointegration and Granger causality tests. Journal of Travel Research 44 (2), 171-176.
177. Lee, J. and Strazicich, M. (2004) Minimum LM Unit Root Test with One Structural Break (Working paper). Boone, NC: Department of Economics, Appalachian State University.
178. Lee, J. and Strazicich, M. C. (2003) Minimum Lagrange multiplier unit root test with two structural breaks. The Review of Economics and Statistics 85 (4), 1082-1089.
179. LeSage, J.P. and Pace, R.K. (2009) Introduction to Spatial Econometrics. Boca Raton, FL: Chapman & Hall/CRC.
180. Lewis, C.D. (1982) Industrial and Business Forecasting Methods. London: Butterworths.
181. Ljung, G.M. and Box, G.E.P. (1978) On a measure of lack of fit in time series models. Biometrika 65, 297-303.
182. Lorenz, E.N. (1963) Deterministic nonperiodic flow. Journal of Atmospheric Sciences 20, 130-141.
183. MacKinnon, J.G. (1996) Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics 11, 601-618.
184. Makridakis, S. and Winkler, R.L. (1983) Averages of forecasts: Some empirical results. Management Science 29, 987-996.
185. Mandelbrot, B.B. (1982) The Fractal Geometry of Nature. San Francisco: W.H. Freeman and Co.
186. Metes, D.V. (2005) Visual, Unit Root and Stationarity Tests and their Power and Accuracy. Summer NSERC student report. On WWW at http://www.stat. ualberta.ca/~ wiens/pubs/metes.pdf. Accessed 11.05.
187. Narayan, P.K. (2005) Testing the unit root hypothesis when the alternative is a trend break stationary process: An application to tourist arrivals in Fiji. Tourism Economics 11 (3), 351-364.
188. Newbold, P. and Granger, C.W.J. (1974) Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society. Series A 137 (2), 131-165.
189. Palm, F. and Zellner, A. (1992) To combine or not to combine? Issues of combining forecasts. Journal of Forecasting 11, 687-701.
190. Papatheodorou, A. and Song, H. (2005) International tourism forecasts: Time- series analysis of world and regional data. Tourism Economics 11 (1), 11-23.
191. Peng, C.K., Havlin, S., Stanley, H.E. and Goldberger, A.L. (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 6, 82-87.
192. Phillips, P.C.B. and Perron, P. (1988) Testing for a unit root in time series regression. Biometrika 75, 335-346.
193. Ravn, M.O. and Uhlig, H. (2002) On adjusting the Hodrick-Prescott filter for the frequency of observations. Review of Economics and Statistics 84 (1), 371-380.
194. Sheldon, PJ. and Var, T. (1985) Tourism forecasting: A review of empirical research. Journal of Forecasting 4, 183-195.
195. Smeral, E. (2007) World tourism forecasting - keep it quick, simple and dirty. Tourism Economics 13 (2), 309-317.
196. Song, H. and Li, G. (2008) Tourism demand modelling and forecasting - a review of recent research. Tourism Management 29, 203-220.
197. Song, H. and Witt, S.F. (eds) (2000) Tourism Demand Modelling and Forecasting: Modern Econometric Approaches. Oxford: Pergamon.
198. Sprott, J.C. (2003) Chaos and Time-Series Analysis. Oxford: Oxford University Press.
199. Taqqu, M., Teverovsky, V. and Willinger, W. (1995) Estimators for long-range dependence: An empirical study. Fractals 3, 785- 798.
200. Tideswell, C., Mules, T. and Faulkner, B. (2001) An integrative approach to tourism forecasting: A glance in the rearview mirror. Journal of Travel Research 40, 162-171.
201. Uysal, M. and Crompton, J.L. (1985) An overview of approaches to forecast tourism demand. Journal of Travel Research 23 (4), 7-15.
202. Wackera, J.G. and Sprague, L.G. (1998) Forecasting accuracy: Comparing the relative effectiveness of practices between seven developed countries. Journal of Operations Management 16 (2-3), 271-290.
203. Waldrop, M. (1992) Complexity: The Emerging Science and the Edge of Order and Chaos. London: Simon and Schuster.
204. Winters, P.R. (1960) Forecasting sales by exponentially weighted moving averages. Management Science 6, 324-342.
205. Witt, S.F. and Witt, C.A. (1992) Modelling and Forecasting Demand in Tourism. London: Academic Press.
206. Witt, S.F. and Witt, C.A. (1995) Forecasting tourism demand: A review of empirical research. International Journal of Forecasting 11, 447-475.
207. Witt, S.F. and Witt, C.A. (2000) Forecasting tourism demand: A review of empirical research. In C.A. Tisdell (ed.) The Economics of Tourism (Vol. 1, pp 141-169) Cheltenham, UK: Edward Elgar.
208. Wong, K.K.F., Song, H., Witt, S.F. and Wu, D.C. (2007) Tourism forecasting: To combine or not to combine? Tourism Management 28, 1068-1078.
209. Zivot, E. and Andrews, D.W.K. (1992) Further evidence on the great crash, the oil-price shock and the unit-root hypothesis. Journal of Business and Economic Statistics 10 (3), 251-270.
210. Aldrich, J. (1997) R.A. Fisher and the making of maximum likelihood 1912-1922Statistical Science 12 (3), 162-176.
211. Baggio, R. (2008) Symptoms of complexity in a tourism system. Tourism Analysis 13 (1), 1-20.
212. Baggio, R., Scott, N. and Cooper, C. (2010) Network science - a review with a focus on tourism. Annals of Tourism Research 37 (3), 802-827.
213. Clauset, A., Shalizi, C.R. and Newman, M.E.J. (2009) Power-law distributions in empirical data. SIAM Review 51 (4), 661-703.
214. Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39 (1), 1-38.
215. Eliason, S.R. (1993) Maximum Likelihood Estimation: Logic and Practice. London:Sage
216. Everitt, B.S. (2006) The Cambridge Dictionary of Statistics (3rd edn). Cambridge: Cambridge University Press.
217. Gabaix, X. (2009) Power laws in economics and finance. Annual Review of Economics 1, 255-293.
218. Goldstein, M.L., Morris, S.A. and Yen, G.G. (2004) Problems with fitting to the power-law distribution. The European Physical Journal B 41, 255-258.
219. Hilbe, J.M. (2009) Logistic Regression Models. Boca Raton, FL: Chapman & Hall/CRC.
220. Huybers, T. (2003) Domestic tourism destination choices - a choice modelling analysis. International Journal of Tourism Research 5 (6), 445-459.
221. Komulainen, T. (2004) Self-similarity and power laws. In H. Hyotyniemi (ed.) Complex Systems - Science on the Edge of Chaos (Report 145, October 2004). Helsinki: Helsinki University of Technology, Control Engineering Laboratory.
222. Le Cam, L. (1990) Maximum likelihood: An introduction. International Statistical Review 58 (2), 153-171.
223. Lew, A.A. (2008) Long tail tourism: New geographies for marketing niche tourism products. Journal of Travel and Tourism Marketing 25 (3), 409-419.
224. Newman, M.E.J. (2005) Power laws, Pareto distributions and Zipf's law. Contemporary Physics 46 (5), 323-351.
225. Pareto, V. (1896) Cours d'Economie Politique. Geneva: Droz.
226. Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992) Numerical Recipes in C: The Art of Scientific Computing (2nd edn). Cambridge: Cambridge University Press.
227. Schneider, T. (2001) Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of Climate 14, 853-871.
228. Stigler, S.M. (2007) The epic story of maximum likelihood. Statistical Science 22 (4), 598-620.
229. Truxillo, C. (2005) Maximum likelihood parameter estimation with incomplete data. Proceedings of the 30th SAS Users Group International Conference, Philadelphia, April 10-13. On WWW at http://www2.sas.com/proceedings/sugi30/111-30.pdf.
230. Ulubasoglu, M.H. and Hazari, B.R. (2004) Zipf's law strikes again: The case of tourism. Journal of Economic Geography 4, 459-472.
231. Ypma, T.J. (1995) Historical development of the Newton-Raphson method. SIAM Review 37 (4), 531-551.
232. Zipf, G.K. (1949) Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Cambridge: Addison-Wesley.
233. Barford, N.C. (1967) Experimental Measurements: Precision, Error and Truth. New York: John Wiley and Sons.
234. Collins, J.C. (2008) Testing, Selection, and Implementation of Random Number Generators (Report: ARL-TR-4498). Aberdeen: MD Army Research Laboratory. On WWW at http://www.arl.army.mil/arlreports/2008/ARL-TR-4498.pdf. Accessed 3.10.
235. Devroye, L. (1986) Nonuniform Variate Generation. New York: Springer-Verlag.
236. Eckhardt, R. (1987) Stan Ulam, John von Neumann, and the Monte Carlo method. Los Alamos Science, Special Issue 15, 131-137.
237. Hull, T.E. and Dobell, A.R. (1962) Random number generators. SIAM Review 4 (3), 230-254.
238. MacKay, D.J.C. (2009) Sustainable Energy - Without the Hot Air. Cambridge: UIT Cambridge.
239. Matsumoto, M. and Nishimura, T. (1998) Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transactions on Modeling and Computer Simulation 8 (1), 3-30.
240. Metropolis, N. (1987) The beginning of the Monte Carlo method. Los Alamos Science, Special Issue 15, 125-130.
241. Metropolis, N. and Ulam, S. (1949) The Monte Carlo method. Journal of the American Statistical Association 44 (247), 335-341.
242. Robert, C.P. and Casella, G. (2004) Monte Carlo Statistical Methods. New York: Springer-Verlag.
243. Sobol, I.M. (1994) A Primer for the Monte Carlo Method. Boca Raton, FL: CRC Press.
244. Taylor, J.R. (1982) An Introduction to Error Analysis. Oxford: Oxford University Press.
245. Axelrod, R. (1997) Advancing the art of simulation in the social sciences. Complexity 3 (2), 16-22.
246. Baggio, R. (2008) Symptoms of complexity in a tourism system. Tourism Analysis 13 (1), 1-20.
247. Bernardes, A.T., Stauffer, D. and Kertesz, J. (2002) Election results and the Sznajd model on Barabasi network. The European Physical Journal B 25, 123-127.
248. Bernstein, S., Lebow, R.N., Stein, J.G. and Weber, S. (2000) God gave physics the easy problems: Adapting social science to an unpredictable world. European Journal of International Relations 7 (1), 43-76.
249. Bodin, O. and Norberg, J. (2005) Information network topologies for enhanced local adaptive management. Environmental Management 35 (2), 175-193.
250. Bonabeau, E. (2002) Agent-based modeling: Methods and techniques for simulating human systems. PNAS 99 (Suppl. 3), 7280-7287.
251. Breckling, B., Middelhoff, U. and Reutera, H. (2006) Individual-based models as tools for ecological theory and application: Understanding the emergence of organisational properties in ecological systems. Ecological Modelling 194 (1-3), 102-113.
252. Cuddington, K.M. and Yodzis, P. (2000) Diffusion-limited predator-prey dynamics in Euclidean environments: An allometric individual-based model. Theoretical Population Biology 58 (4), 259-278.
253. DeAngelis, D.L. and Mooij, W.M. (2005) Individual-based modeling of ecological and evolutionary processes. Annual Review of Ecology, Evolution and Systematics 36, 147-168.
254. Doran, C.F. (1999) Why forecasts fail: The limits and potential of forecasting in international relations and economics. International Studies Review 1 (2), 11-41.
255. Farmer, J.D. and Foley, D. (2009) The economy needs agent-based modelling. Nature 460, 685-686.
256. Farrell, B.H. and Twining-Ward, L. (2004) Reconceptualizing tourism. Annals of Tourism Research 31 (2), 274-295.
257. Faulkner, B. and Russell, R. (1997) Chaos and complexity in tourism: In search of a new perspective. Pacific Tourism Review 1, 93-102.
258. Galan, J.M., Izquierdo, L.R., Izquierdo, S.S., Santos, J.I., del Olmo, R., Lopez- Paredes, A. and Edmonds, B. (2009) Errors and artefacts in agent-based modelling. Journal of Artificial Societies and Social Simulation 12 (1).
259. Garson, G.D. (2009) Computerized simulation in the social sciences: A survey and evaluation. Simulation and Gaming 40 (2), 267-279.
260. Gilbert, N. and Terna, P. (2000) How to build and use agent-based models in social science. Mind & Society 1 (1), 52-72.
261. Hartmann, S. (1996) The world as a process: Simulations in the natural and social sciences. In R. Hegselmann, U. Mueller and K.G. Troitzsch (eds) Modelling and Simulation in the Social Sciences: From the Philosophy of Science Point of View (pp 77-100). Dordrecht: Kluwer.
262. Henrickson, L. and McKelvey, B. (2002) Foundations of 'new' social science: Institutional legitimacy from philosophy, complexity science, postmodernism, and agent-based modeling. PNAS 99 (Suppl 3), 7288-7295.
263. Holling, C.S. (2001) Understanding the complexity of economic, ecological, and social systems. Ecosystems 4 (5), 390-405.
264. Holling, C.S. (2004) From complex regions to complex worlds. Ecology and Society 9 (1), 11.
265. Hovel, K.A. and Regan, H.M. (2008) Using an individual-based model to examine the roles of habitat fragmentation and behavior on predator-prey relationships in seagrass landscapes. Landscape Ecology 23 (Suppl. 1), 75-89.
266. Iozzi, F. (2008) A Simple Implementation of Schelling's Segregation Model in NetLogo (Dondena Working Paper No. 15): 'Carlo F. Dondena' Centre for Research on Social Dynamics, Bocconi University. On WWW at http://www.dondena.unibocconi.it/wp15. Accessed 9.09.
267. Ising, E. (1925) Beitrag zur Theorie des Ferromagnetismus. Zeitschrift fur Physik 31, 253-258.
268. Lansing, J.S. (2003) Complex adaptive systems. Annual Review of Anthropology 32, 183-204.
269. Levin, S.A. (2002) Complex adaptive systems: Exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society 40 (1), 3-19.
270. Macy, M.W. and Willer, R. (2002) From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology 28, 143-166.
271. Mazanec, J.A., Wober, K. and Zins, A.H. (2007) Tourism destination competitiveness: From definition to explanation? Journal of Travel Research 46, 86-95.
272. McKercher, B. (1999) A chaos approach to tourism. Tourism Management 20, 425-434.
273. Nonaka, E. and Holme, P. (2007) Agent-based model approach to optimal foraging in heterogeneous landscapes: effects of patch clumpiness. Ecography 30 (6), 777-788.
274. Papatheodorou, A. and Song, H. (2005) International tourism forecasts: Time- series analysis of world and regional data. Tourism Economics 11 (1), 11-23.
275. Peck, S.L. (2008) The hermeneutics of ecological simulation. Biology and Philosophy 23 (3), 383-402.
276. Pike, S. (2002) Destination image analysis - a review of 142 papers from 1973 to 2000. Tourism Management 23, 541-549.
277. Poincare, H. (1892-1899) Les Me'thodes nouvelles de la mecanique ceUeste. Paris: Gauthier-Villars.
278. Schelling, T.C. (1969) Models of segregation. The American Economic Review 59 (2), 488-493.
279. Schelling, T.C. (1971) Dynamic models of segregation. Journal of Mathematical Sociology 1, 143-186.
280. Schulze, C. (2003) Advertising effects in Sznajd marketing model. International Journal of Modern Physics C 14 (1), 95-98.
281. Silvert, W. (2001) Modelling as a discipline. International Journal of General Systems 30 (3), 261-282.
282. Song, H. and Li, G. (2008) Tourism demand modelling and forecasting - A review of recent research. Tourism Management 29, 203-220.
283. Song, H. and Witt, S.F. (eds) (2000) Tourism Demand Modelling and Forecasting: Modern Econometric Approaches. Oxford: Pergamon.
284. Srbljinovic, A. and Skunca, O. (2003) An introduction to agent based modelling and simulation of social processes. Interdisciplinary Description of Complex Systems 1 (1-2), 1-8.
285. Sznajd-Weron, K. and Sznajd, J. (2000) Opinion evolution in closed community. International Journal of Modern Physics C 11 (6), 1157-1165.
286. Sznajd-Weron, K. and Weron, R. (2002) A simple model of price formation. International Journal of Modern Physics C 13 (1), 115-123.
287. Taber, C.S. and Timpone, R.J. (1996) Computational Modeling (Vol. 113). Thousand Oaks, CA: Sage.
288. Tideswell, C., Mules, T. and Faulkner, B. (2001) An integrative approach to tourism forecasting: A glance in the rearview mirror. Journal of Travel Research 40, 162-171.
289. Ulubasoglu, M.H. and Hazari, B.R. (2004) Zipf's law strikes again: The case of tourism. Journal of Economic Geography 4, 459-472.
290. UNWTO (2009) Compendium of Tourism Statistics. Madrid: World Tourism Organization.
291. Wackera, J.G. and Sprague, L.G. (1998) Forecasting accuracy: Comparing the relative effectiveness of practices between seven developed countries. Journal of Operations Management 16 (2-3), 271-290.
292. Waldrop, M. (1992) Complexity: The Emerging Science and the Edge of Order and Chaos. New York: Simon and Schuster.
293. Weins, J.A. (1997) Metapopulation dynamics and landscape ecology. In I.A. Hanski and M.E. Gilpin (eds) Metapopulation Biology, Ecology, Genetics, and Evolution. New York: Academic Press.
294. Wilensky, U. (1997) NetLogo Segregation model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. On WWW at http://ccl.northwestern.edu/netlogo/models/Segregation.
295. Wilensky, U. (1999) NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. On WWW at http://ccl.northwestern.edu/netlogo.
296. Wilson, W.G. (1998) Resolving discrepancies between deterministic population models and individual-based simulations. The American Naturalist 151 (2), 116-134.
297. Annigeri, S. (2009) Scilab - A Hands on Introduction (3rd edn). Free ebook. On WWW at http://www.lulu.com/product/ebook/scilab—a-hands-on- introduction/6418827.
298. Arbuckle, J. (2008) AMOS 17.0 User's Guide. Chicago, IL: SPSS Inc.
299. Braun, W.J. (2008) A First Course in Statistical Programming with R. Cambridge: Cambridge University Press.
300. Crawley, M.J. (2007) The R Book. New York: John Wiley & Sons.
301. Croudace, T., Dunn, G. and Pickles, A. (2010) General Latent Variable Modelling Using Mplus. Boca Raton, FL: Chapman & Hall/CRC.
302. Doornik, J.A. and Ooms, M. (2006) Introduction to Ox. Free ebook. On WWW at http://www.doornik.com/ox/OxIntro.pdf.
303. Eaton, J.W., Bateman, D. and Hauberg, S. (2008) GNU Octave Manual - Version 3. Bristol, UK: Network Theory Ltd.
304. Elliott, R.J. (2000) Learning SAS in the Computer Lab (2nd edn). Pacific Grove, CA: Duxbury Press.
305. Faul, F., Erdfelder, E., Lang, A-G. and Buchner, A. (2007) G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39, 175-191.
306. Field, A. (2005) Discovering Statistics Using SPSS (2nd edn). London: Sage.
307. Grier, D.A. (2005) When Computers Were Human. Princeton, NJ: Princeton University Press.
308. Hamilton, L.C. (2008) Statistics with STATA (6th edn). Pacific Grove, CA: Duxbury Press.
309. Hanselman, D.C. and Littlefield, B.L. (2004) Mastering Matlab 7. Upper Saddle River, NJ: Prentice-Hall.
310. Hegger, R., Kantz, H. and Schreiber, T. (1999) Practical implementation of nonlinear time series methods: The TISeAn package. CHAOS 9, 413.
311. Kelloway, E.K. (1998) Using LISRELfor Structural Equation Modeling: A Researcher's Guide. London: Sage.
312. LeSage, J.P. and Pace, R.K. (2009) Introduction to Spatial Econometrics. Boca Raton, FL: Chapman & Hall/cRc.
313. Lin, K-P. (2001) Computational Econometrics: GAUSS Programming for Econometricians and Financial Analysts. Carson, CA: ETEXT.
314. Lutz, M. and Ascher, D. (2003) Learning Python (2nd edn). Sebastopol, CA: O'Reilly Media.
315. McCullough, B.D. and Wilson, B. (2002) On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Computational Statistics & Data Analysis 40, 713- 721.
316. Open Mx (2010) OpenMx User Guide. On WWW at http://openmx.psyc.virgi- nia.edu/.
317. Railsback, S.F. and Grimm, V. (2009) A Course in Individual- and Agent-based Modeling - Scientific Modeling with NetLogo. In preparation, preliminary version on WWW at http://www.railsback-grimm-abm-book.com/.
318. Ryan, B.F., Joiner, B.L. and Cryer, J.D. (2005) MINITAB Handbook (5th edn). Pacific Grove, CA: Brooks/Cole - Thomson Learning Inc.
319. Simon, J.L. (1997) Resampling: The New Statistics. On WWW at http://www. resample.com/.
320. Triola, M.F. (2006) Elementary Statistics Using Excel (3rd edn). Boston, MA: Addison Wesley.
321. Vogelvang, B. (2005) Econometrics: Theory & Applications With Eviews. Edinburgh, UK: Pearson Education - Prentice-Hall.