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The objective of this study was to estimate the good-of-fitness and precision of parameters of the Gompertz-Laird, Logistic, Richards, and Von Bertalanffy growth models, using different data collection periods (DCP). Two hundred and sixty-two Mexican Creole chicks (116 females and 146 males), were individually weighed to form the following sets of data for each sex: DCP 1 (weights recorded weekly from hatching to 63 d, and every 2 wk, from 63 to 133 d of age), DCP 2 (weights recorded weekly from hatching to 133 d of age), DCP 3 (weights recorded every third day, from hatching to 63 d, and every 14 d, from 63 to 133 d of age), and DCP 4 (weights recorded every third day, from hatching to 63 d, and weekly, from 63 to 133 d of age). Data were analyzed using the NLIN procedure of SAS (Marquardt algorithm). For all growth models, the width of confidence interval (CI) of each parameter, was estimated (α = 0.05). The adjusted coefficient of determination ( AR 2), as well as the Akaike ( AIC ) and Bayesian information criteria ( BIC) were used to select the best model. The higher the AR2 , and the lower the width of CI , as well as the AIC and BIC values, the better the model. The Gompertz-Laird model, more frequently showed the highest AR2 , and the lowest AIC and BIC values compared to the other models. Moreover, for all models, both sexes and all parameters, most confidence interval widths (all with the Gompertz-Laird model) were the lowest with DCP 3 when compared to the other sets of data. In conclusion, the Gompertz-Laird model was the best provided that the chickens are weighed every third day from hatching until 63 d of age, and every 2 wk thereafter. [ABSTRACT FROM AUTHOR]