A primer of methods in biobehavioral research: Improving a study's design, analysis, and write up

The Maze Book: Theories, Practice, and Protocols for Testing Rodent Cognition

Volumn , Issue , 2015, Pages 323-374

  Talboom, Joshua S ^a,b   West, Stephen G ^c   Bimonte Nelson, Heather A ^c  

^a SUN HEALTH RESEARCH INSTITUTE   (United States)

^b ARIZONA ALZHEIMER S CONSORTIUM   (United States)

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Analysis; Best practices; Biobehavioral; Data; Design; Format; Methods; Research; Statistics; Style; Write up

Indexed keywords

EID: 85020958090     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4939-2159-1_12     Document Type: Chapter

References (71)

1. American Psychological Association (2010) Publication manual of the American Psychological Association, 6th edn. American Psychological Association, Washington, DC
2. Baraldi AN, Enders CK (2010) An introduction to modern missing data analyses. J Sch Psychol 48(1):5-37. doi:10.1016/j.jsp.2009.10.001
3. Barnett V, Lewis T (1994) Outliers in statistical data, vol 3. Wiley, New York, NY
4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate - a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57(1):11, Retrieved from http://www.jstor.org.ezproxy2.library.arizona.edu/stable/2346101
5. Borenstein M (2009) Effect sizes for continuous data. In: Cooper HM, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis, 2nd edn. Russell Sage, New York, NY, p 615
6. Brady JV (1958) Ulcers in "executive" monkeys. Sci Am 199(3):9. doi:10.1038/scientificamerican1058-95
7. Brown MB, Forsythe AB (1974) Robust tests for the equality of variances. J Am Stat Assoc 69(346):3. doi:10.1080/01621459.1974.10482955
8. Cleveland WS (1993) Visualizing data. Hobart Press, Lafayette, IN
9. Cleveland WS (1994) The elements of graphing data, 2nd edn. Hobart Press, Lafayette, IN
10. Cliff N (1996) Ordinal methods for behavioral data analysis. Erlbaum, Mahwah, NJ
11. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale, NJ
12. Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Lawrence Erlbaum Associates, Mahwah, NJ
13. Cook TD, Campbell DT (1979) Quasi-experimentation: design & analysis issues for field settings. Rand McNally College Publishing Company, Chicago, IL
14. Coxe S, West SG, Aiken LS (2009) The analysis of count data: a gentle introduction to Poisson regression and its alternatives. J Pers Assess 91(2):15. doi:10.1080/00223890802634175
15. Cumming G (2012) Understanding the new statistics: effect sizes, confidence intervals, and meta-analysis. Routledge, New York, NY
16. Cumming G, Finch S (2005) Inference by eye: confidence intervals and how to read pictures of data. Am Psychol 60(2):10. doi:10.1037/0003-066X.60.2.170
17. Curran-Everett D (2013) Explorations in statistics: the analysis of ratios and normalized data. Adv Physiol Educ 37(3):213-219. doi:10.1152/advan.00053.2013
18. Curran-Everett D, Benos DJ (2007) Guidelines for reporting statistics in journals published by the American Physiological Society: the sequel. Adv Physiol Educ 31(4):295-298. doi:10.1152/advan.00022.2007
19. Dean A, Voss D (1999) Design and analysis of experiments. Springer, New York, NY
20. Denenberg VH (1996) A primer for behavioral research. Ment Retard Dev Disabil Res Rev 2(4):7. doi:10.1002/(SICI)1098-2779(1996)2:4<209::AID-MRDD4>3.0.CO;2-L
21. Edwards JR (2001) Ten difference score myths. Organ Res Methods 4(3):22. doi:10.1177/109442810143005
22. Eich E (2014) Business not as usual. Psychol Sci 25:3. doi:10.1177/0956797613512465
23. Enders CK (2010) Applied missing data analysis. Guilford Press, New York, NY
24. Fleiss J, Berlin J (2009) Effect sizes for dichotomous data. In: Cooper HM, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis, 2nd edn. Russell Sage, New York, NY, p 615
25. Franz VH (2007) Ratios: a short guide to confidence limits and proper use [7-6-14]. ARXIV 60. doi:2007arXiv0710.2024F
26. Franz VH, Loftus GR (2012) Standard errors and confidence intervals in within-subjects designs: generalizing and avoiding the biases of alternative accounts. Psychon Bull Rev 19(3):395-404. doi: 10.3758/s13423-012-0230-1
27. Harlow LL, Mulaik SA, Steiger JH (1997) What if there were no significance tests? Lawrence Erlbaum Associates, Mahwah, NJ
28. Hochberg Y, Tamhane AC (1987) Multiple comparison procedures. Wiley, New York, NY
29. Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):15. doi:10.1080/01621459.1986.10478354
30. Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods, 3rd edn. Wiley, Hoboken, NJ
31. Hsu JC (1996) Multiple comparisons: theory and methods. Chapman & Hall, New York, NY
32. Huitema BE (2011) The analysis of covariance and alternatives: statistical methods for experiments, quasi-experiments, and single-case studies, 2nd edn. Wiley, Hoboken, NJ
33. Hyde LA, Hoplight BJ, Denenberg VH (1998) Water version of the radial-arm maze: learning in three inbred strains of mice. Brain Res 785(2):236-244. doi:10.1016/S0006-8993(97)01417-0
34. Kelley K (2007) Methods for the behavioral, educational, and social sciences: an R package. Behav Res Methods 39(4):979-984. doi:10.3758/BF03192993
35. Kelley K, Maxwell SE (2012) Sample size planning. In: Cooper HM, American Psychological Association (eds) APA handbook of research methods in psychology. American Psychological Association, Washington, DC
36. Keppel G, Wickens TD (2004) Design and analysis: a researcher's handbook, 4th edn. Pearson Prentice Hall, Upper Saddle River, NJ
37. Levin JR, Serlin RC, Seaman MA (1994) A controlled, powerful multiple-comparison strategy for several situations. Psychol Bull 115(1):153-159. doi:10.1037//0033-2909.115.1.153
38. Levine TR, Hullett CR (2002) Eta squared, partial eta squared, and misreporting of effect size in communication research. Hum Commun Res 28(4):612-625. doi:10.1111/j.1468-2958.2002.tb00828.x
39. Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, Hoboken, NJ
40. Loftus GR, Masson ME (1994) Using confidence intervals in within-subject designs. Psychon Bull Rev 1(4):476-490. doi:10.3758/BF03210951
41. Long JS (1997) Regression models for categorical and limited dependent variables. Sage Publications, Thousand Oaks, CA
42. MacCallum RC, Zhang S, Preacher KJ, Rucker DD (2002) On the practice of dichotomization of quantitative variables. Psychol Methods 7(1):19-40. doi:10.1037/1082-989X.7.1.19
43. MacKinnon D (2008) Introduction to statistical mediation analysis. Routledge, New York, NY
44. Maxwell SE, Delaney HD (2004) Designing experiments and analyzing data, 2nd edn. Psychological Press, New York, NY
45. Olejnik S, Algina J (2000) Measures of effect size for comparative studies: applications, interpretations, and limitations. Contemp Educ Psychol 25(3):241-286. doi:10.1006/ceps.2000.1040
46. Olejnik S, Algina J (2003) Generalized eta and omega squared statistics: measures of effect size for some common research designs. Psychol Methods 8(4):13. doi:10.1037/1082-989X.8.4.434
47. Osborne J, Waters E (2002) Four assumptions of multiple regression that researchers should always test. Pract Assess Res Eval 8(2):1-9, Retrieved from http://ericae.net/pare/getvn.asp?v=8&n=2
48. Rosenthal R (1991) Meta-analytic procedures for social research, vol 6 Sage Publications, Newbury Park, CA
49. Rosenthal R, Rosnow RL (1985) Contrast analysis: focused comparisons in the analysis of variance. Cambridge University Press, New York, NY
50. Rosopa PJ, Schaffer MM, Schroeder AN (2013) Managing heteroscedasticity in general linear models. Psychol Methods 18(3):16. doi:10.1037/a0032553
51. Ruxton GD, Colegrave N (2011) Experimental design for the life sciences, 3rd edn. Oxford University Press, Oxford, NY
52. Schwarzkopf DS, De Haas B, Rees G (2012) Better ways to improve standards in brain-behavior correlation analysis. Front Hum Neurosci 6:200. doi:10.3389/fnhum.2012.00200
53. Seber GAF, Lee AJ (2003) Linear regression analysis, 2nd edn. Wiley-Interscience, Hoboken, NJ
54. Shadish WR, Cook TD, Campbell DT (2001) Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin, Boston, MA
55. Shiffler RE (1988) Maximum Z scores and outliers. Am Stat 42(1):11. doi:10.1080/00031305.198.10475530
56. Stevens JP (2007) Intermediate statistics: a modern approach. Lawrence Erlbaum Associates, Mahwah, NJ
57. Stevens SS (1946) On the theory of scales of measurement. Science 103(2684):677-680. doi:10.1126/science.103.2684.677
58. Swinscow TDV, Campbell MJ (2002) Statistics at square one, 11th edn. BMJ Books, London
59. Thalheimer W, Cook S (2002) How to calculate effect sizes from published research: a simplified methodology. 9. Retrieved from http://www.work-learning.com/welcome.html
60. Vargha A, Delaney HD (1998) The Kruskal-Wallis test and stochastic homogeneity. J Educ Behav Stat 23(2):22. doi:10.3102/10769986023002170
61. Velleman PF, Wilkinson L (1993) Nominal, ordinal, interval, and ratio typologies are misleading. Am Stat 47(1):7. doi:10.1080/00031305.1993.10475938
62. Warner RM (2008) Applied statistics: from bivariate through multivariate techniques. Sage Publications, Los Angeles, CA
63. Weisberg S (2005) Applied linear regression, 3rd edn. Wiley-Interscience, Hoboken, NJ
64. West SG, Cham H, Liu Y (2014) Causal inference and generalization in field settings: experimental and quasi-experimental designs. In: Reis HT, Judd CM (eds) Handbook of research methods in personality and social psychology, 2nd edn. Cambridge University Press, New York, NY, pp 49-80
65. West SG, Thoemmes F (2010) Campbell's and Rubin's perspectives on causal inference. Psychol Methods 15(1):18-37. doi:10.1037/a0015917
66. Westfall PH, Tobias RD, Wolfinger RD (2011) Multiple comparisons and multiple tests using SAS, 2nd edn. SAS Institute Inc., Cary, NC
67. Wilcox RR (2012) Introduction to robust estimation and hypothesis testing, 3rd edn. Academic, Boston, MA
68. Wilcox RR (2012) Modern statistics for the social and behavioral sciences: a practical introduction. CRC Press, Boca Raton, FL
69. Wilcox RR, Keselman HJ (2003) Modern robust data analysis methods: measures of central tendency. Psychol Methods 8(3):254-274. doi:10.1037/1082-989X.8.3.254
70. Winer BJ, Brown DR, Michels KM (1991) Statistical principles in experimental design, 3rd edn. McGraw-Hill, New York, NY
71. Yang M, Maxwell SE (2014) Treatments effects in randomized longitudinal dropout. Psychol Methods 19(2):188-210