SavedAdd your journal

Journals / Tarım Bilimleri Dergisi / 2018 / Cilt: 24 - Sayı: 2

Multilevel Analysis for Repeated Measures Data in Lambs1

Suna AKKOL
Ferda KARAKUŞ
Fırat CENGİZ
Pages
218–226
Publication Date
2018-06-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of the study, individual growth curves can be investigated using multilevel modeling in animal studies which is an important parameter of the individual growth rate.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The study was conducted to compare the individual growth curves models and to detect individual differences in the growth rate by a performing multilevel analysis.
📊
Key Findings & Data:The data set used for this purpose consisted of live weight records of 52 crossbred lambs from birth to 182 days of age.
💡
Conclusion & Impact:According to the results of the study, individual growth curves can be investigated using multilevel modeling in animal studies which is an important parameter of the individual growth rate.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Repeated data · Multilevel models · Intra-class correlation

Abstract

The study was conducted to compare the individual growth curves models and to detect individual differences in the growth rate by a performing multilevel analysis. The data set used for this purpose consisted of live weight records of 52 crossbred lambs from birth to 182 days of age. There were 670 observations in level-1 units which were the repeated measurements over time, and there were 52 observations in level-2 units which were lambs. In the study, parameter estimation of timeindependent covariate factors, such as gender, birth type and birth weight, was performed by using five different models within the framework of multilevel modeling. LRT, AIC and BIC were used for the selection of the best model. The “Conditional Quadratic Growth Model-B” provided the best fit to the data set. The multilevel analysis indicated that linear and quadratic growth in lambs was significant. According to the results of the study, individual growth curves can be investigated using multilevel modeling in animal studies which is an important parameter of the individual growth rate.

🔖

Cite This Article

AKKOL, S., KARAKUŞ, F., CENGİZ, F. (2018). Multilevel Analysis for Repeated Measures Data in Lambs1. Tarım Bilimleri Dergisi, Cilt: 24 - Sayı: 2, 218–226. https://ajindex.com/articles/178559

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. Akaike H (1974). A new look at the statistical model identification. IEEE Transaction on Automatic Control 19(6): 716-724 Bryk A S & Raudenbush S W (1986). A hierarchical model for studying school effects. Sociology of Education 59: 1-17 Chen H & Cohen P (2006). Using individual growth model to analyze the change in quality of life from adolescence to adulthood. Health and Quality of Life Outcomes 4: 10 DeLucia C & Pitts S C (2006). Applications of individual growth curve modelling for pediatric psychology research. Journal of Pediatric Psychology 31(10): 1002-1023
  2. Dudley W N, McGuire D B, Peterson D E & Wong B (2009). Application of multilevel growth-curve analysis in cancer treatment toxicities: The exemplar of oral mucositis and pain. Oncology Nursing Forum 36(1): 11-19
  3. Goldstein H (1999). Multilevel statistical models project. Available at http://www.ats.ucla.edu/stat/examples/ msm_goldstein/goldstein.pdf (Accessed on February 5, 2015)
  4. Green L E, Berriatua E & Morgan K L (1998). A multilevel model of data with repeated measures of the effect of lamb diarrhoea on weight. Preventive Veterinary Medicine 36: 85-94
  5. Gulliford M C, Ukoumunne O C & Chinn S (1999). Components of variance and intraclass correlations for the design of community-based surveys and intervention studies. American Journal of Epidemiology 149: 876-883
  6. Hedeker D (2004). An Introduction to Growth Modelling. In: D Kaplan (Ed), The Sage Handbook of Quantitative Methodology for the Social Sciences. Sage Publications, Thousand Oaks, CA Hedeker D & Gibbons R D (2006). Longitudinal data analysis. New York: Wiley. ISBN: 978-0-471-42027-9
  7. Hox J J (2010). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum Ip E H, Wasserman R & Barkin S (2011). Comparison of intraclass correlation coefficient estimates and standard errors between using cross-sectional and repeated measurement data: The safety check cluster randomized trial. Contemporary Clinical Trials 32(2): 225-232 doi:10.1016/j.cct.2010.11.001 Kristjansson S D, Kircher J & Webb A K (2007). Multilevel models for repeated measures research designs in psychophysiology: An introduction to growth curve modelling. Psychophysiology 44: 728-736
  8. Lancelot R, Lesnoff M, Tillard E & McDermott J J (2000). Graphical approaches to support the analysis of linear-multilevel models of lamb pre-weaning growth in Kolda (Senegal). Preventive Veterinary Medicine 46: 225-247
  9. Leeden R V D (1998). Multilevel analysis of repeated measures data. Qualit