The priority of just-identified, recursive models

Psychological Methods

Volumn 7, Issue 3, 2002, Pages 307-315

  Reichardt, Charles S ^a  

^a UNIVERSITY OF DENVER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; METHODOLOGY; NOTE; PSYCHOLOGICAL MODEL; PSYCHOLOGY;

HUMANS; MODELS, PSYCHOLOGICAL; PSYCHOLOGY;

MLCS; MLOWN;

EID: 85047671340     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/1082-989X.7.3.307     Document Type: Article

References (15)

1. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
2. Cook, T. D. (1985). Postpositivist critical multiplism. In R. L. Shotland & M. M. Mark (Eds.), Social science and social policy (pp. 21-62). Beverly Hills, CA: Sage.
3. Duncan, O. D. (1975). Introduction to structural equation models. New York: Academic Press.
4. Gollob, H. F., & Reichardt, C. S. (1987). Taking account of time lags in causal models. Child Development, 58, 80-92.
5. Gollob, H. F., & Reichardt, C. S. (1991). Interpreting and estimating indirect effects assuming time lags really matter. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 243-259). Washington, DC: American Psychological Association.
6. Johnston, J. (1984). Econometric methods (3rd ed.). New York: McGraw-Hill.
7. MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-226.
8. McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64-82.
9. Meehl, P. E., & Waller, N. G. (2002). The path analysis controversy: A new statistical approach to strong appraisal of verisimilitude. Psychological Methods, 7, 283-300.
10. Reichardt, C. S., & Gollob, H. F. (1986). Satisfying the constraints of causal modeling. In W. M. K. Trochim (Ed.), New directions for program evaluation: Vol. 31. Advances in quasi-experimental design and analysis (pp. 91-107). San Francisco: Jossey-Bass.
11. Reichardt, C. S., & Gollob, H. F. (1987). Taking uncertainty into account when estimating effects. In M. M. Mark & R. L. Shotland (Eds.), New directions for program evaluation: Vol. 35. Multiple methods for program evaluation (pp. 7-22). San Francisco: Jossey-Bass.
12. Reichardt, C. S., & Gollob, H. F. (1997). When confidence intervals should be used instead of statistical tests, and vice versa. In L. L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 259-284). Hillsdale, NJ: Erlbaum.
13. Shadish, W. R., & Cook, T. D. (1999). Comment - Design rules: More steps toward a complete theory of quasi-experimentation. Statistical Science, 14, 294-300.
14. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.
15. Theil, H. (1971). Principles of econometrics. New York: Wiley.