Psychonomic Bulletin & Review

, Volume 11, Issue 5, pp 791–806 | Cite as

Likelihood ratios: A simple and flexible statistic for empirical psychologists

Theoretical And Review Articles
  • 721 Downloads

Abstract

Empirical studies in psychology typically employ null hypothesis significance testing to draw statistical inferences. We propose that likelihood ratios are a more straightforward alternative to this approach. Likelihood ratios provide a measure of the fit of two competing models; the statistic represents a direct comparison of the relative likelihood of the data, given the best fit of the two models. Likelihood ratios offer an intuitive, easily interpretable statistic that allows the researcher great flexibility in framing empirical arguments. In support of this position, we report the results of a survey of empirical articles in psychology, in which the common uses of statistics by empirical psychologists is examined. From the results of this survey, we show that likelihood ratios are able to serve all the important statistical needs of researchers in empirical psychology in a format that is more straightforward and easier to interpret than traditional inferential statistics.

Supplementary material

Glover-PBR-2004.zip (74 kb)
Supplementary material, approximately 340 KB.

References

  1. Adolphs, R., &Tranel, D. (1999). Preferences for visual stimuli following amygdala damage.Journal of Cognitive Neuroscience,11, 610–616.PubMedCrossRefGoogle Scholar
  2. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.),Second international symposium on information theory (pp. 267–281). Budapest: Académiai Kiadó.Google Scholar
  3. Arbuthnott, K., &Frank, J. (2000). Executive control in set switching: Residual switch cost and task-set inhibition.Canadian Journal of Experimental Psychology,54, 33–41.PubMedGoogle Scholar
  4. Chochon, F., Cohen, L., van de Moortele, P., &Dehaene, S. (1999). Differential contributions of the left and right inferior parietal lobules to number processing.Journal of Cognitive Neuroscience,11, 617–630.PubMedCrossRefGoogle Scholar
  5. Cohen, J. (1977).Statistical power analysis for the behavioral sciences (Rev. ed.). New York: Academic Press.Google Scholar
  6. De Gennaro, L., Ferrara, M., Urbani, L., &Bertini, M. (2000). A complementary relationship between wake and REM sleep in the auditory system: A pre-sleep increase of middle-ear muscle activity (MEMA) causes a decrease of MEMA during sleep.Experimental Brain Research,130, 105–112.CrossRefGoogle Scholar
  7. Diedrichsen, J., Ivry, R., Cohen, A., &Danziger, S. (2000). Asymmetries in a unilateral flanker task depend on the direction of the response: The role of attentional shift and perceptual grouping.Journal of Experimental Psychology: Human Perception & Performance,26, 113–126.CrossRefGoogle Scholar
  8. Dixon, P. (2001, June).The logic of pro forma statistics. Poster presented at the meeting of the Canadian Society for Brain, Behaviour, and Cognitive Science, Quebec.Google Scholar
  9. Edwards, A. W. F. (1972).Likelihood. London: Cambridge University Press.Google Scholar
  10. Estes, W. K. (1997). On the communication of information by displays of standard errors and confidence intervals.Psychonomic Bulletin & Review,4, 330–341.CrossRefGoogle Scholar
  11. Fisher, R. A. (1925).Statistical methods for research workers. New York: Hafner.Google Scholar
  12. Fisher, R. A. (1955). Statistical methods and scientific induction.Journal of the Royal Statistical Society: Series B,17, 69–78.Google Scholar
  13. Fugelsang, J. A., &Thompson, V. (2000). Strategy selection in causal reasoning: When beliefs and covariation collide.Canadian Journal of Experimental Psychology,54, 15–32.PubMedGoogle Scholar
  14. Goodman, S. N., &Royall, R. (1988). Evidence and scientific research.American Journal of Public Health,78, 1568–1574.PubMedCrossRefGoogle Scholar
  15. Hoffmann, E. A., &Haxby, J. (2000). Distinct representations of eye gaze and identity in the distributed human neural system for face perception.Nature Neuroscience,3, 80–84.CrossRefGoogle Scholar
  16. Hurvich, C. M., &Tsai, C.-L. (1989). Regression and time series model selection in small samples.Biometrika,76, 297–307.CrossRefGoogle Scholar
  17. Judd, C. M., &McClelland, G. H. (1989).Data analysis: A modelcomparison Approach. San Diego: Harcourt Brace Jovanovich.Google Scholar
  18. Kinoshita, S. (2000). The left-to-right nature of the masked onset priming effect in naming.Psychonomic Bulletin & Review,7, 133–141.CrossRefGoogle Scholar
  19. Loftus, G. R. (1996). Psychology will be a much better science when we change the way we analyze data.Current Directions in Psychological Science,5, 161–171.CrossRefGoogle Scholar
  20. Loftus, G. R. (2001). Analysis, interpretation, and visual presentation of data. In H. Pashler & J. Wixted (Eds.),Stevens’ Handbook of experimental psychology (3rd ed., pp. 339–390). New York: Wiley.Google Scholar
  21. Lykken, D. E. (1968). Statistical significance in psychological research.Psychological Bulletin,70, 151–159.PubMedCrossRefGoogle Scholar
  22. Masson, M. E. J., &Loftus, G. R. (2003). Using confidence intervals for graphically based data interpretation.Canadian Journal of Experimental Psychology,57 203–220.PubMedGoogle Scholar
  23. Neyman, J., &Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference.Biometrika,20, 175–240, 263–294.Google Scholar
  24. Neyman, J., &Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses.Philosophical Transactions of the Royal Society of London: Series A,231, 289–337.CrossRefGoogle Scholar
  25. Pitt, M. A., Myung, I. J., &Zhang, S. (2002). Toward a method of selecting among computational models of cognition.Psychological Review,109, 472–491.PubMedCrossRefGoogle Scholar
  26. Prabhakaran, V., Narayanan, K., Zhao, Z., &Gabrieli, J. (2000). Integration of diverse information in working memory within the frontal lobe.Nature Neuroscience,3, 85–90.PubMedCrossRefGoogle Scholar
  27. Rissanen, J. (1996). Fisher information and stochastic complexity.IEEE Transactions on Information Theory,42, 40–47.CrossRefGoogle Scholar
  28. Royall, R. M. (1997).Statistical evidence: A likelihood paradigm. London: Chapman & Hall.Google Scholar
  29. Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test.Psychological Bulletin,57, 416–428.PubMedCrossRefGoogle Scholar
  30. Schwartz, G. (1978). Estimating the dimension of a model.Annals of Statistics,6, 461–464.CrossRefGoogle Scholar
  31. Servos, P. (2000). Distance estimation in the visual and visuomotor systems.Experimental Brain Research,130, 35–47.CrossRefGoogle Scholar
  32. Sivia, D. S. (1996).Data analysis: A Bayesian tutorial. Oxford: Oxford University Press.Google Scholar
  33. Soto-Faraco, S. (2000). An auditory repetition deficit under low memory-load.Journal of Experimental Psychology: Human Perception & Performance,26, 264–278.CrossRefGoogle Scholar
  34. Tryon, W. W. (2001). Evaluating statistical difference, equivalence, and indeterminacy using inferential confidence intervals: An integrated alternative method of conducting null hypothesis statistical tests.Psychological Methods,6, 371–386.PubMedCrossRefGoogle Scholar
  35. Zheng, Y., Myerson, J., &Hale, S. (2000). Age and individual differences in visuospatial processing speed: Testing the magnification hypothesis.Psychonomic Bulletin & Review,7, 113–120.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2004

Authors and Affiliations

  1. 1.Department of PsychologyRoyal Holloway University of LondonEghamEngland
  2. 2.University of AlbertaEdmontonCanada

Personalised recommendations