Skip to main content Accessibility help
×
Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-29T15:42:28.056Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 April 2016

Bill Shipley
Affiliation:
Université de Sherbrooke, Canada
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Cause and Correlation in Biology
A User's Guide to Path Analysis, Structural Equations and Causal Inference with R
, pp. 290 - 296
Publisher: Cambridge University Press
Print publication year: 2016

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

Aldrich, J. (1995). Correlations genuine and spurious in Pearson and Yule. Statistical Science 10: 364–76.Google Scholar
Bentler, P. M. (1995). EQS Structural Equations Program Manual, Version 3.0. Los Angeles, BMDP Statistical Software.
Bentler, P. M., and Bonnett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin 88: 588–606.Google Scholar
Bernard, C. (1865). Introduction à l’étude de la médicine expérimentale. Paris, J. B. Baillière.
Beveridge, W. I. B. (1957). The Art of Scientific Investigation. New York, Random House.
Blalock, H. M. (1961). Correlation and causality: the multivariate case. Social Forces 39: 246–51.Google Scholar
Blalock, H. M. (1964). Causal Inferences in Nonexperimental Research. Chapel Hill, University of North Carolina Press.
Blomberg, S. P., Lefevre, J. G., Wells, J. A., and Waterhouse, M. (2012). Independent contrasts and PGLS regression estimators are equivalent. Systematic Biology 61: 382–91.Google Scholar
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York, Wiley.
Bollen, K. A., and Long, J. S. (1993). Testing Structural Equation Models. Newbury Park, CA, Sage.
Bollen, K. A., and Stine, R. A. (1993). Bootstrapping goodness-of-fit measures in structural equation models, in Bollen, K. A., and Long, J. S. (eds.), Testing Structural Equation Models: 111–34. Newbury Park, CA, Sage.
Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology 37: 62–83.Google Scholar
Browne, M. W., and Cudeck, R. (1993). Alternative ways of assessing model fit, in Bollen, K. A., and Long, J. S. (eds.), Testing Structural Equation Models: 136–62. Newbury Park, CA, Sage.
Bumpus, H. C. (1899). The elimination of the unfit as illustrated by the introduced sparrow. Biological Lectures Delivered at the Marine Biological Laboratory of Woods Hole 6: 209–26.Google Scholar
Burke, J. (1996). The Pinball Effect: How Renaissance Water Gardens Made the Carburetor Possible – and Other Journeys through Knowledge. Boston, Little, Brown.
Cleveland, W. S., and Devlin, S. J. (1988). Locally-weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association 83: 596–610.Google Scholar
Cleveland, W. S., Devlin, S. J., and Grosse, E. (1988). Regression by local fitting. Journal of Econometrics 37: 87–114.Google Scholar
Cleveland, W. S., Grosse, E., and Shyu, W. M. (1992). Local regression models, in Chambers, J. M., and Hastie, T. J. (eds.), Statistical Models in S: 309–76. Pacific Grove, CA, Wadsworth & Brooks.
Conover, W. J., and Iman, R. L. (1981). Rank transformations as a bridge between parametric and nonparametric statistics. American Statistician 35: 124–9.Google Scholar
Cowan, I. R., and Farquhar, G. D. (1977). Stomatal function in relation to leaf metabolism environment, in Jennings, D. H. (ed.), Integration of Activity in the Higher Plant: 471–505. Cambridge University Press.
Cowles, M., and Davis, C. (1982a). Is the .05 level subjectively reasonable? Canadian Journal of Behavioural Sciences 14: 248–52.Google Scholar
Cowles, M., and Davis, C. (1982b). On the origins of the .05 level of statistical significance. American Psychologist 37: 553–8.Google Scholar
D'Agostino, R. B., Belanger, A., and D'Agostino, R. B. J. (1990). A suggestion for using powerful and informative tests of normality. American Statistician 44: 316–21.Google Scholar
Davenport, C. B. (1917). Inheritance of stature. Genetics 2: 313–89.Google Scholar
Davis, W. R. (1993). The FC1 rule of identification for confirmatory factor analysis. Sociological Methods and Research 21: 403–37.Google Scholar
De Robertis, E. D. P., and De Robertis, E. M. F. (1980). Cell and Molecular Biology. Boston, Thomson Learning.
DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological Methods 2: 292–307.Google Scholar
Duhem, P. (1914). La théorie physique: Son objet, sa structure. Paris, Rivière.
Dunn, G., Everitt, B., and Pickles, A. (1993). Modelling Covariances and Latent Variables Using EQS. London, Chapman & Hall.
Eliason, S. R. (1993). Maximum Likelihood Estimation: Logic and Practice. Newbury Park, CA, Sage.
Epstein, R. J. (1987). A History of Econometrics. New York, Elsevier Science.
Farebrother, R. (1987). Algorithm AS 231: the distribution of a noncentral chi-square variable with nonnegative degrees of freedom. Applied Statistics 36: 402–5.Google Scholar
Feiblman, J. K. (1972). Scientific Method. The Hague, Martinus Nijhoff.
Felsenstein, J. (1985). Phylogenies and the comparative method. American Naturalist 125: 1–15.Google Scholar
Fisher, F. M. (1970). A correspondence principle for simultaneous equation models. Econometrica 38: 73–92.Google Scholar
Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh, Oliver & Boyd.
Fisher, R. A. (1926). The Design of Experiments. Edinburgh, Oliver & Boyd.
Fisher, R. A. (1950). Contributions to Mathematical Statistics. New York, Wiley.
Fisher, R. A. (1959). Smoking: The Cancer Controversy. Edinburgh, Oliver & Boyd.
Fisher, R. A. (1970). The Design of Experiments, 8th edn. New York, Hafner.
Forrest, D. W. (1974). Francis Galton: The Life and Work of a Victorian Genius. New York, Taplinger.
Galton, F. (1869). Hereditary Genius: An Inquiry into Its Laws and Consequences. London, Macmillan.
Geiger, D., Verma, T., and Pearl, J. (1990). Identifying independence in Bayesian networks. Networks 20: 507–34.Google Scholar
Glymour, G., Scheines, R., Spirtes, R., and Kelly, K. (1987). Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling. Orlando, Academic Press.
Goldberger, A. S. (1972). Structural equation methods in the social sciences. Econometrica 40: 979–1002.Google Scholar
Good, P. (1993). Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. New York, Springer.
Good, P. (1994). Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, 2nd edn. New York, Springer.
Grace, J. B. (2006). Structural Equation Modeling and Natural Systems. Cambridge University Press.
Grace, J. B., and Bollen, K. A. (2008). Representing general theoretical concepts in structural equation models: the role of composite variables. Environmental and Ecological Statistics 15:191–213.Google Scholar
Griliches, Z. (1974). Errors in variables and other unobservables. Econometrica 42: 971–98.Google Scholar
Grime, J. P. (1979). Plant Strategies and Vegetation Processes. New York, Wiley.
Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica 11: 1–12.Google Scholar
Harvey, P. H., and Pagel, M. D. (1991). The Comparative Method in Evolutionary Biology. Oxford University Press.
Hastie, T. J., and Tibshirani, R. (1990). Generalized Additive Models. London, Chapman & Hall.
Heise, D. (1975). Causal Analysis. New York, Wiley.
Hoogland, J. J., and Boomstra, A. (1998). Robustness studies in covariance structure modelling: an overview and a meta-analysis. Sociological Methods and Research 26: 239–367.Google Scholar
Hotelling, H. (1953). New light on the correlation coefficient and its transformations. Journal of the Royal Statistical Society, Series B 15: 193–232.Google Scholar
Howson, C., and Urbach, P. (1989). Scientific Reasoning: The Bayesian Approach. La Salle, IL, Open Court.
Hox, J. J. (1993). Factor analysis of multilevel data: gauging the Muthén model, in Oud, J. H. L., and van Blokland-Vogelsang, R. A. W. (eds.), Advances in Longitudinal and Multivariate Analysis in the Behavioural Sciences: 141–56. Nijmegen, ITS.
Jobson, J. D. (1992). Applied Multivariate Data Analysis, vol. I, Regression and Experimental Design. New York, Springer.
Jordano, P. (1995). Frugivore-mediated selection on fruit and seed size: birds and St. Lucie's cherry, Prunus mahaleb. Ecology 76: 2627–39.Google Scholar
Jöreskog, K. G. (1967). Some contributions to maximum likelihood factor analysis. Psychometrika 32: 443–82.Google Scholar
Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34: 183–202.Google Scholar
Jöreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika 57: 239–51.Google Scholar
Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system, in Goldberger, A. S., and Duncan, O. D. (eds.), Structural Equation Models in the Social Sciences: 85–112. New York, Academic Press.
Keesling, J. W. (1972). Maximum likelihood approaches to causal analysis, PhD thesis. University of Chicago.
Kempthorpe, O. (1979). The Design and Analysis of Experiments. Huntington, NY, Robert E. Krieger.
Kendall, M. G., and Gibbons, J. D. (1990). Rank Correlation Methods. New York, Oxford University Press.
Kendall, M. G., and Stuart, A. (1983). The Advanced Theory of Statistics. London, Charles Griffin.
Kikuzawa, K. (1995). The basis for variation in leaf longevity of plants. Vegetatio 121: 89–100.Google Scholar
Korn, E. L. (1984). The ranges of limiting values of some partial correlations under conditional independence. American Statistician 38: 61–2.Google Scholar
Lande, R., and Arnold, S. J. (1983). The measurement of selection on correlated characters. Evolution 37: 1210–26.Google Scholar
Li, C. C. (1975). Path Analysis: A Primer. Pacific Grove, CA, Boxwood Press.
Little, R. J. A., and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd edn. Hoboken, NJ, Wiley.
Mach, E. (1883). The Science of Mechanics: A Critical and Historical Account of Its Development, 5th edn, with revisions from 9th German edn. La Salle, IL, Open Court.
Manly, B. F. J. (1997). Randomization, Bootstrap and Monte Carlo Methods in Biology, 2nd edn. London, Chapman & Hall.
Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika 57: 519–30.Google Scholar
Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya, Series B 36: 115–28.Google Scholar
Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979). Multivariate Analysis. London, Academic Press.
Martins, E. P., and Hansen, T. F. (1997). Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the anlaysis of interspecific data. American Naturalist 149: 646–67.Google Scholar
Mayo, D. G. (1996). Error and the Growth of Experimental Knowledge. Chicago University Press.
McDonald, R. P. (1994). The bilevel reticular action model for path analysis with latent variables. Sociological Methods and Research 22: 399–413.Google Scholar
Meziane, D. (1998). Étude de la variation interspécifique de la vitesse spécifique de croissance et modélisation de l'effet des attributs morphologiques, physiologiques et d'allocation de biomasse, PhD thesis. Université de Sherbrooke.
Mulaik, S. A. (1986). Toward a synthesis of deterministic and probabilistic formulations of causal relations by the functional relation concept. Philosophy of Science 53: 313–32.Google Scholar
Muthén, B. O. (1990). Mean and Covariance Structure Analysis of Hierarchical Data, Statistical Series paper no. 62. Los Angeles, University of California.
Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods and Research 22: 376–98.Google Scholar
Muthén, B. O. (1997). Latent variable modeling of longitudinal and multilevel data, in Raftery, A. E. (ed.), Sociological Methodology 1997: 453–81. Washington, DC, American Sociological Association.
Muthén, B. O., and Satorra, A. (1995). Complex sample data in structural equation modeling, in Marsden, P. V. (ed.), Sociological Methodology: 267–316. Washington, DC, American Sociological Association.
Niles, H. E. (1922). Correlation, causation and Wright's theory of ‘path coefficients’. Genetics 7: 258–73.Google Scholar
Norton, B. J. (1975). Biology and philosophy: the methodological foundations of biometry. Journal of the History of Biology 8: 85–93.Google Scholar
Passmore, J. (1966). A Hundred Years of Philosophy. Harmondsworth, Penguin Books.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, Morgan Kaufmann.
Pearl, J. (1997). The new challenge: from a century of statistics to an age of causation. Computing Science and Statistics 29: 415–23.Google Scholar
Pearl, J. (2000). Causality. Cambridge University Press.
Pearl, J., and Dechter, R. (1996). Identifying independencies in causal graphs with feedback, in Horvitz, E., and Jensen, F. V. (eds.), Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence: 240–6. San Francisco, Morgan Kaufmann.
Pearson, E. S., and Kendall, M. G. (1970). Studies in the History of Statistics and Probability. London, Griffin.
Pearson, K. (1892). The Grammar of Science. London, Adam & Charles Black.
Pearson, K. (1911). The Grammar of Science, 3rd edn. London, Adam & Charles Black.
Peters, R. H. (1991). A Critique for Ecology. Cambridge University Press.
Pollack, J. L. (1986). Contemporary Theories of Knowledge. Totowa, NJ, Rowman & Littlefield.
Popper, K. (1980). The Logic of Scientific Discovery. London, Hutchinson.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. (1986). Numerical Recipes: The Art of Scientific Computing. Cambridge University Press.
Provine, W. B. (1986). Sewall Wright and Evolutionary Biology. University of Chicago Press.
Pugesek, B. H., and Tomer, A. (1996). The Bumpus house sparrow data: a reanalysis using structural equation models. Evolutionary Ecology 10: 387–404.Google Scholar
Rao, M. M. (1984). Probability Theory with Applications. Orlando, Academic Press.
Rapport, S., and Wright, T. (1963). Science: Method and Meaning. New York University Press.
Richardson, T. (1996a). A discovery algorithm for directed cyclic graphs, in Horvitz, E., and Jensen, F. V. (eds.), Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence: 454–61. San Francisco, Morgan Kaufmann.
Richardson, T. (1996b). Models of feedback: interpretation and discovery. PhD thesis, Pittsburgh, Carnegie Mellon University.
Rigdon, E. E. (1995). A necessary and sufficient identification rule for structural models estimated in practice. Multivariate Behavioral Research 30: 359–83.Google Scholar
Rosseel, Y. (2012). lavaan: an R package for structural equation modeling. Journal of Statistical Software 48: 1–36.Google Scholar
Rubin, D. B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association 57: 473–89.Google Scholar
Santos, J. C., and Cannetella, D. C. (2011). Phenotypic integration emerges from aposematism and scale in poison frogs. Proceedings of the National Association of Science 108: 6175–80.Google Scholar
Satorra, A., and Bentler, P. M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis, in Proceedings of the Business and Economic Statistics Section: Papers Presented at the Annual Meeting of the American Statistical Association: 308–13. Alexandria, VA, American Statistical Association.
Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London, Chapman & Hall.
Scott, A. J., and Holt, D. (1982). The effect of two-stage sampling on ordinary least squares methods. Journal of the American Statistical Association 77: 848–54.Google Scholar
Shipley, B. (1995). Structured interspecific determinants of specific leaf area in 34 species of herbaceous angiosperms. Functional Ecology 9: 312–19.Google Scholar
Shipley, B. (1997). Exploratory path analysis with applications in ecology and evolution. American Naturalist 149: 1113–38.Google Scholar
Shipley, B. (1999). Exploring hypothesis space: examples from organismal biology, in Glymour, C., and Cooper, G. F. (eds.), Computation, Causation, and Discovery: 441–52. Menlo Park, CA, AAAI Press.
Shipley, B. (2000). A new inferential test for path models based on directed acyclic graphs. Structural Equation Modeling 7: 206–18.Google Scholar
Shipley, B. (2009). Confirmatory path analysis in a generalized multilevel context. Ecology 90: 363–8.Google Scholar
Shipley, B., and Hunt, R. (1996). Regression smoothers for estimating parameters of growth analyses. Annals of Botany 76: 569–76.Google Scholar
Shipley, B., and Lechowicz, M. J. (2000). The functional coordination of leaf morphology and gas exchange in 40 wetland plant species. Ecoscience 7: 183–94.Google Scholar
Shipley, B., Lechowicz, M. J., Wright, I. J., and Reich, P. B. (2006). Fundamental trade-offs generating the worldwide leaf economics spectrum. Ecology 87: 535–41.Google Scholar
Shipley, B., and Peters, R. H. (1990). A test of the Tilman model of plant strategies: relative growth rate and biomass partitioning. American Naturalist 136: 139–53.Google Scholar
Shirahata, S. (1980). Rank tests of partial correlation. Bulletin of Mathematical Statistics 19: 9–18.Google Scholar
Simon, H. (1977). Models of Discovery. Dordrecht, D. Reidel.
Sokal, R. R., and Rohlf, F. J. (1981). Biometry. New York, Freeman.
Spearman, C. (1904). General intelligence objectively determined and measured. American Journal of Psychology 15: 201–93.Google Scholar
Spirtes, P. (1995). Directed cyclic graphical representation of feedback models, in Besnard, P., and Hanks, S. (eds.), Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence: 491–8. San Francisco, Morgan Kaufmann.
Spirtes, P., Glymour, C., and Scheines, R. (1990). Causality from probability, in McGee, G. (ed.), Evolving Knowledge in Natural Science and Artificial Intelligence: 181–99. London, Pitman.
Spirtes, P., Glymour, C., and Scheines, R. (1993). Causation, Prediction, and Search. New York, Springer.
Spirtes, P., Richardson, T., Meek, C., and Scheines, R. (1998). Using path diagrams as a structural equation modeling tool. Sociological Methods and Research 27: 182–225.Google Scholar
Steiger, J. H. (1989). EzPATH: A Supplementary Manual for SYSTAT and SYGRAPH. Evanston, IL, SYSTAT Inc.
Steiger, J. H. (1990). Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research 25: 173–80.Google Scholar
Tanaka, J. S. (1993). Multifaceted conceptions of fit in structural equation models, in Bollen, K. A., and Long, J. S. (eds.), Testing Structural Equation Models: 10–39. Newbury Park, CA, Sage.
Van Buuren, S., and Groothuis-Oudshoorn, K. (2011). Multivariate imputation by chained equations. Journal of Statistical Software 45: 1–67.Google Scholar
Van Hulst, R. (1979). On the dynamics of vegetation: Markov chains as models of succession. Vegetatio 40: 3–14.Google Scholar
Verma, T., and Pearl, J. (1988). Causal networks: semantics and expressiveness, in Shachter, R., Levitt, T., Kanal, L. N., and Lemmer, J. F. (eds.), Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence: 352–9. New York, Elsevier Science.
Verma, T., and Pearl, J. (1990). Equivalence and synthesis of causal models, in Bonissone, P. P., Henrion, M., Kanal, L. N., and Lemmer, J. F. (eds.), Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence: 255–68. New York, Elsevier Science.
Von Hardenberg, A., and Gonzalez-Voyer, A. (2012). Disentangling evolutionary cause–effect relationships with phylogenetic confirmatory path analysis. Evolution 67: 378–87.Google Scholar
Wahba, G. (1991). Spline Models for Observational Data. Philadelphia, SIAM Press.
Wishart, J. (1928). Sampling errors in the theory of two factors. British Journal of Psychology 19: 180–7.Google Scholar
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont, B. B., Lee, T., Lee, W., Lusk, C., Midgley, J. J., Navas, M.-L., Niinemets, Ü., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V. I., Roumet, C., Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J., and Villar, R. (2004). The worldwide leaf economics spectrum. Nature 428: 821–7.Google Scholar
Wright, S. (1918). On the nature of size factors. Genetics 3: 367–74.Google Scholar
Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea pigs. Proceedings of the National Academy of Science 6: 320–32.Google Scholar
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research 10: 557–85.Google Scholar
Wright, S. (1925). Corn and Hog Correlations, USDA Bulletin no. 1300. Washington, DC, US Department of Agriculture.
Wright, S. (1984). Diverse uses of path analysis, in Chakravarti, A., Human Population Genetics: 1–34. New York, Van Nostrand Reinhold.

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Bill Shipley, Université de Sherbrooke, Canada
  • Book: Cause and Correlation in Biology
  • Online publication: 05 April 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781139979573.012
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Bill Shipley, Université de Sherbrooke, Canada
  • Book: Cause and Correlation in Biology
  • Online publication: 05 April 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781139979573.012
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Bill Shipley, Université de Sherbrooke, Canada
  • Book: Cause and Correlation in Biology
  • Online publication: 05 April 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781139979573.012
Available formats
×