A structural equation modeling approach for the analysis of cortisol data collected using pre-post-post designs

Structural Equation Modeling

Volumn 14, Issue 1, 2007, Pages 125-145

  Willoughby, Michael ^a   Vandergrift, Nathan ^a   Blair, Clancy ^b   Granger, Douglas A ^b  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NOVEL APPLICATIONS; STRUCTURAL EQUATION MODELING;

DESIGN;

CORTISOL;

EID: 33847252847     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1207/s15328007sem1401_7     Document Type: Article

References (42)

1. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
2. Arminger, G., & Stein, P. (1997). Finite mixtures of covariance structure models with regressors. Sociological Methods and Research, 26, 148-182.
3. Arminger, G., Stein, P., & Wittenberg, J. (1999). Mixtures of conditional mean and covariance structure models. Psychometrika, 64, 475-494.
4. Blair, C., Granger, D., & Razza, R. P. (2005). Cortisol reactivity is positively related to executive function in preschool children attending Head Start. Child Development, 76, 554-567.
5. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
6. Bollen, K. A. (2001). Two-stage least squares and latent variable models: Simultaneous estimation and robustness to misspecifications. In R. Cudeck, S. Du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future (pp. 119-138). Lincolnwood, IL: Scientific Software.
7. Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley.
8. Browne, M. W., & Du Toit, S. H. C. (1991). Models for learning data. In L. M. Collins & J. L. Horn (Ed.), Best methods for the analysis of change: Recent advances, unanswered questions, future direc-tions (pp. 47-68). Washington, DC: American Psychological Association.
9. Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach for incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421-483.
10. Dickerson, S. S., & Kemeny, M. E. (2004). Acute Stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130, 355-391.
11. Erickson, K., Drevets, W., & Schulkin, J. (2003). Glucocorticoid regulation of diverse cognitive functions in normal and pathological emotional states. Neuroscience and Biobehavioral Reviews, 27, 233-246.
12. Granger, D., & Kivlighan, K. T. (2003). Integrating biological, behavioral, and social levels of analysis in early child development: Progress, problems, and prospects. Child Development, 74, 1058-1063.
13. Granger, D. A., Kivlighan, K. T., Blair, C., El-Sheikh, M., Mize, J., Lisonbee, J. A., et al. (2006). Integrating the measurement of salivary alpha-amylase into studies of child health, development, and social relationships. Journal of Social and Personal Relationships, 23, 267-290.
14. Granger, D., Stansbury, K., & Henker, B. (1994). Preschoolers' behavioral and neuroendocrine responses to social challenge. Merrill Palmer Quarterly, 40, 190-211.
15. Gunnar, M., & White, B. P. (2001). Salivary cortisol measures in infant and child assessment. In L. T. Singer & P. S. Zeskind (Eds.), Biobehavioral assessment of the infant (pp. 167-189). New York: Guilford.
16. Heim, C., Ehlert, U., & Hellhammer, D. H. (2000). The potential role of hypocortisolism in the pathophysiology of stress-related bodily disorders. Psychoneuroendocrinology, 25, 1-35.
17. Jacques, S., & Zelazo, P. D. (2001). The Flexible Item Selection Task (FIST): A measure of executive function in preschoolers. Developmental Neuropsychology, 20, 573-591.
18. Jedidi, K., Jagpal, H., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16, 39-59.
19. Kam, C., & Greenberg, M. (2001). A version of the teacher observation of classroom adaptation for use with preschoolers. State College: Pennsylvania State University.
20. Klein, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford.
21. Lupien, S. J., & McEwen, B. S. (1997). The acute effects of corticosteroids on cognition: Integration of animal and human model studies. Brain Research Reviews, 24, 1-27.
22. MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-226.
23. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 84-99.
24. McArdle, J. J. (1989). A structural modeling experiment with multiple growth functions. In R. Kanter. E. Phillip, L. Ackerman, & R. Cudeck (Eds.), Abilities, motivation, and methodology: The Minnesota Symposium on Learning and Individual Differences (pp. 71-117). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
25. McEwen, B. S. (2000). The neurobiology of stress: From serendipity to clinical relevance. Brain Research, 886, 172-189.
26. Meredith, W., & Tisak, J. (1984, July). Tuckerizing curves. Paper presented at the annual meeting of the Psychometric Society, Santa Barbara, CA.
27. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
28. Muthén, B. O. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In L. M. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 291-322). Washington. DC: American Psychological Association.
29. Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.
30. Muthén, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
31. Muthén, L. K., & Muthén, B. O. (2004). Mplus users guide (3rd ed.). Los Angeles: Author.
32. Ramsay, D. S., & Lewis, M. (1994). Developmental change in infant cortisol and behavioral response to inoculation. Child Development, 65, 1491-1502.
33. Ramsay, D., & Lewis, M. (2003). Reactivity and regulation in cortisol and behavioral responses to stress. Child Development, 74, 456-464.
34. Rao, C. R. (1958). Some statistical methods for comparison of growth curves. Biometrics, 14, 1-17.
35. Sayer, A. G., & Cumsille, P. E. (2001). Second-order latent growth models. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 179-201). Washington, DC: American Psychological Association.
36. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
37. Schwartz, E., Granger, D., Susman, E. J., Gunnar, M., & Laird, B. (1998). Assessing salivary cortisol in studies of child development. Child Development, 69, 1503-1513.
38. Tucker, L. R. (1958). Determination of parameters of a functional relation by factor analysis. Psychometrika, 23, 19-23.
39. Werthamer-Larrson, L., Kellam, S. G., & Wheeler, L. (1991). Effect of first grade classroom environment on shy behavior, aggressive behavior, and concentration problems. American Journal of Community Psychology, 19, 890-897.
40. Willet, J. B. (1997). Measuring change: What individual growth modeling buys you. In E. Amsel & K. A. Renninger (Eds.), Change and development: Issues of theory, method, and application (pp. 213-243). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
41. Woody, E., & Sadler, P. (2005). Structural equation models for interchangeable dyads: Being the same makes a difference. Psychological Methods, 10, 139-158.
42. Yung, Y. (1997). Finite mixtures in confirmatory factor analysis models. Psychometrika, 62, 297-330.