Statistical inference involving binomial and negative binomial parameters

Spanish Journal of Psychology

Volumn 12, Issue 1, 2009, Pages 288-307

  García Pérez, Miguel A ^a   Núñez Antón, Vicente ^b  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^b UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Binomial parameters; Effect size; Monte Carlo simulations; Power; Statistical tests

Indexed keywords

ARTICLE; BINOMIAL DISTRIBUTION; EQUIPMENT DESIGN; HUMAN; METHODOLOGY; MONTE CARLO METHOD; PROBABILITY; RESEARCH; SAMPLE SIZE; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS; VISUAL DISORDER;

BINOMIAL DISTRIBUTION; DATA INTERPRETATION, STATISTICAL; EQUIPMENT DESIGN; HUMANS; LOGISTIC MODELS; MODELS, STATISTICAL; MONTE CARLO METHOD; PROBABILITY; RESEARCH; SAMPLE SIZE; STATISTICS AS TOPIC; VISION DISORDERS;

EID: 67449104623     PISSN: 11387416     EISSN: 19882904     Source Type: Journal    
DOI: 10.1017/S1138741600001694     Document Type: Article

References (16)

1. Apfelbaum, H. L., Apfelbaum, D. H., Woods, R. L., & Peli, E. (2008). Inattentional blindness and augmented-vision displays: Effects of cartoon-like filtering and attended scene. Ophthalmic and Physiological Optics, 28, 204-217. (Pubitemid 351569409)
2. Bain, L. J., & Engelhardt, M. (1992). Introduction to probability and mathematical statistics (2nd edition). Pacific Grove, CA: Duxbury.
3. Clark-Carter, D. (1997). The account taken of statistical power in research published in the British Journal of Psychology. British Journal of Psychology, 88, 71-83.
4. Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
5. García-Pérez, M. A., & Núñez-Antón, V. (2004). Small-sample comparisons for goodness-of-fit statistics in one-way multinomials with composite hypotheses. Journal of Applied Statistics, 31, 161-181. (Pubitemid 38188017)
6. Larntz, K. (1978). Small-sample comparisons of exact levels for chi-squared goodness-of-fit statistics. Journal of the American Statistical Association, 73, 253-263.
7. The MathWorks, Inc. (2004). MATLAB: The language of technical computing. Natick, MA: Author.
8. Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods, 9, 147-163. (Pubitemid 38706222)
9. Neisser, U., & Becklen, R. (1975). Selective looking: Attending to visually specified events. Cognitive Psychology, 7, 480-494.
10. Numerical Algorithms Group (1999). NAG Fortran library manual, Mark 19. Oxford: Author.
11. Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1986). Numerical recipes: The art of scientific computing. New York: Cambridge University Press.
12. Riefer, D. M., & Batchelder, W. H. (1991). Statistical inference for multinomial processing tree models. In J.-P. Doignon & J.-C. Falmagne (Eds.), Mathematical psychology: Current developments (pp. 313-335). New York: Springer.
13. Rudas, T. (1986). A Monte Carlo comparison of the small sample behaviour of the Pearson, the likelihood ratio and the Cressie-Read statistics. Journal of Statistical Computation and Simulation, 24, 107-120.
14. Serlin, R. C., & Harwell, M. R. (2004). More powerful tests of predictor subsets in regression analysis under nonnormality. Psychological Methods, 9, 492-509. (Pubitemid 40005045)
15. Visual Numerics, Inc. (1997). IMSL math/library special functions. Houston, TX: Author.
16. Wolfram, S. (1992). Mathematica: A system for doing mathematics by computer (2nd edition). New York: Addison-Wesley.