1 Huitema, BE. Analysis of Covariance and Alternatives. New York: John Wiley %26 Sons Inc.
; 1980, 445.
2 Milliken, GA, Johnson, DE. Analysis of Messy Data. Volume III: Analysis of Covariance
. Boca Raton, FL: Chapman %26 Hall/CRC
; 2002, 605.
3 Fisher, RA. Statistical Methods for Research Workers
. 4th ed.
Edinburgh, Scotland: Oliver and Boyd
4 Underwood, AJ. Experiments in Ecology
. Cambridge: Univeristy Press
; 1997, 504.
5 Riggs, MR, Haroldson, KJ, Hanson, MR. Analysis of covariance models for data from observational field studies. J Wild Manage 2008, 72:34–43. doi: 10.2193/ 2007‐315.
6 Barrett, TJ, Tingley, MA, Munkittrick, KR, Lowell, RB. Dealing with heterogeneous regression slopes in analysis of covariance: new methodology applied to environmental effects monitoring fish survey data. Environ Monit Assess 2010, 166:279–291. doi: 10.1007/ s10661‐009‐1001‐y.
7 Leon, AC, Portera, L, Lowell, K, Rheinheimer, D. A strategy to evaluate a covariate by group interaction in an analysis of covariance. Psychopharmacol Bull 1998, 34:805–809.
8 Zinbarg, RE, Suzuki, S, Uliaszek, AA, Lewis, AR. Biased parameter estimates and inflated type I error rates in analysis of covariance (and analysis of partial variance) arising from unreliability: alternatives and remedial strategies. J Abnorm Psychol 2010, 119:307–319. doi: 10.1037/a0017552.
9 Gujarati, D. Use of dummy variables in testing for equality between sets of coefficients in two linear regressions: a note. Am Stat 1970, 24:50–52.
10 Fox, J. Applied Regression Analysis, Linear Model, and Related Models
. Thousand Oaks, CA: Sage Publications, Inc.
; 1997, 597.
11 García‐Berthou, E. On the misuse of residuals in ecology: testing regression residuals vs. the analysis of covariance. J Anim Ecol 2001, 70:708–711.
12 Lowell, RB, Kilgour, BW. Interpreting effluent effects on fish when the magnitude of effect changes with size or age of fish. Can Tech Rep Fish Aquat Sci 2008, 2793:82–83.
13 Quinn, GP, Keough, MJ. Experimental Design and Data Analysis for Biologists. Cambridge: Univeristy Press
; 2002, 537.
14 Johnson, PO, Neyman, J. Tests of certain linear hypotheses and their application to some educational problems. Stat Res Mem 1936, 1:57–93.
15 Wilcox, RR. Pairwise comparisons of j
independent regression lines over a finite interval, simultaneous pairwise comparisons of their parameter, and the Johnson–Neyman procedure. Brit J Math and Stat Psy 1987, 40:80–93.
16 Myers, JL, Well, AD. Research Design and Statistical Analysis
. Mahwah, NJ: Lawrence Erlbaum Associates
; 2003, 855.
17 Olejnik, SF, Algina, J. A review of nonparametric alternatives to analysis of covariance. Evaluation Rev 1985, 9:51–83.
18 Quade, D. Rank analysis of covariance. J Am Stat Assoc 1967, 62:1187–1200.
19 Puri, ML, Sen, PK. Analysis of covariance based on general rank scores. Ann Math Stat 1969, 40:610–618.
20 McSweeny, M, Porter, AC. Small sample properties of nonparametric index of response and rank analysis of covariance
. Occasional paper No. 16, Office of Research Consultation. East Lansing, MI: State University
21 Burnett, TD, Barr, DR. A non‐parametric analogy of analysis for covariance. Educ Psychol Meas 1997, 37:341–348.
22 Shirley, EAC. A distribution‐free method for analysis of covariance based on ranked data. Appl Stat 1981, 30:158–162.
23 Conover, WJ, Iman, RL. Analysis of covariance using the rank transformation. Biometrics 1982, 38:715–724.
24 Rheinheimer, DC, Penfield, DA. The effects of type I error rate and power of the ANCOVA F Test and selected alternatives under nonnormality and variance heterogeneity. J Exp Educ 2001, 69:373–391.
25 Hamilton, BL. A Monte Carlo test of robustness of parametric and nonparametric analysis of covariance against unequal regression slopes. J Am Stat Assoc 1976, 71:864–869.
26 Abunnaja, SS. The robustness of parametric and non‐parametric analysis of covariance against unequal regression slopes and non‐normality. Presented at the meeting of the American Educational Research Association in Montreal, Canada: 1980.