Implications of Recent Developments in Structural Equation Modeling for Counseling Psychology

The Counseling Psychologist

Volumn 27, Issue 4, 1999, Pages 485-527

  Quintana, Stephen M ^a   Maxwell, Scott E ^b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0033238686     PISSN: 00110000     EISSN: None     Source Type: Journal    
DOI: 10.1177/0011000099274002     Document Type: Article

References (83)

1. Allison, P. D. (1987). Estimation of linear models with incomplete data. In C. C. Clogg (Ed.), Sociological methodology (pp. 71-103). San Francisco: Jossey-Bass.
2. Aquino, J. A., Russell, D. W., Cutrona, C. E., & Altmaier, E. A. (1996). Employment status, social support, and life satisfaction among the elderly. Journal of Counseling Psychology, 43, 480-489.
3. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumaker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates.
4. Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238-246.
5. Bentler, P. M. (1997). EQS: Structural equations program manual. Encino, CA: Multivariate Software.
6. Bentler, P. M., & Chou, C. -P. (1987). Practical issues in structural equation modeling. Sociological Methods and Research, 16, 78-117.
7. Bentler, P. M., & Chou, C. -P. (1988). Practical issues in structural equation modeling. In J. S. Long (Ed.), Common problems/proper solutions: Avoiding error in quantitative research (pp. 161-192). Newbury Park, CA: Sage.
8. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley Interscience.
9. Bollen, K. A., & Barb, K. H. (1981). Pearson–s r and coarsely categorized measures. American Sociological Review, 46, 232-239.
10. Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110, 305-314.
11. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equations models (pp. 136-162). Newbury Park, CA: Sage.
12. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage.
13. Busemeyer, J. R., & Jones, L. E. (1983). Analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93, 549-562.
14. Chou, C. -P., & Bentler, P. M. (1995). Estimates and tests in structural equation modeling. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 37-55). Thousand Oaks, CA: Sage.
15. Cohen, J. (1978). Partialed products are interactions; partialed powers are curve components. Psychological Bulletin, 85, 858-866.
16. Cole, D. A., Maxwell, S. E., Arvey, R., & Salas, E. (1993). Multivariate group comparisons of variables systems: MANOVA and structural equations modeling. Psychological Bulletin, 114, 174-184.
17. Cook, R. D., & Weisberg, S. (1994). An introduction to regression graphics. New York: Wiley.
18. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.
19. Cudeck, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 105, 317-327.
20. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16-29.
21. Daniel, C., & Wood, F. S. (1980). Fitting equations to data. New York: Wiley.
22. Duncan, T. E., Duncan, S. C., & Stoolmiller, M. (1994). Modeling developmental processes using latent growth structural equation methodology. Applied-Psychological-Measurement, 18, 343-354.
23. Fassinger, R. E. (1987). Use of structural equations modeling in counseling psychology research. Journal of Counseling Psychology, 34, 425-436.
24. Fox, J. (1997). Applied regression analysis, linear models, and related methods. Thousand Oaks, CA: Sage.
25. Garfield, S. L. (1989). The practice of brief psychotherapy. New York: Pergamon.
26. Gerbing, D. W., & Anderson, J. C. (1993). Monte Carlo evaluations of goodness-of-fit indices for structural equations models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equations models (pp. 40-65). Newbury Park, CA: Sage.
27. Gollob, H. F., & Reichardt, C. S. (1991). In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 92-105). Washington, DC: American Psychological Association.
28. Henry, W. P., Schacht, T. E., & Strupp, H. H. (1986). Structural analysis of social behavior: Application to a study of interpersonal process in differential psychotherapeutic outcome. Journal of Consulting and Clinical Psychology, 54(1), 27-31.
29. Heppner, P. P., Kivlighan, D. M., Jr., & Wampold, B. E. (1999). Research design in counseling (2nd ed.). Pacific Grove, CA: Brooks/Cole.
30. Houts, A. C., Cook, T. D., & Shadish, W. R. (1986). The person-situation debate: A critical multiplist perspective. Journal of Personality, 54, 52-87.
31. Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 1-15). Thousand Oaks, CA: Sage.
32. Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158-176). Thousand Oaks, CA: Sage.
33. Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76-99). Thousand Oaks, CA: Sage.
34. Jaccard, J., & Wan, C. K. (1995). Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: Multiple indicator and structural equation approaches. Psychological Bulletin, 117, 348-357.
35. Joreskog, K. G. (1993). Testing structural equations models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equations models (pp. 294-316). Newbury Park, CA: Sage.
36. Joreskog, K. G., & Sorbom, D. (1988). PRELIS: A program for multivariate data screening and data summarization: A preprocessor for LISREL (2nd ed.). Chicago: Scientific Software.
37. Joreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques. Mahwah, New Jersey: Lawrence Erlbaum Associates.
38. Kenny, D. A. (1979). Correlation and causality. New York: Wiley Interscience.
39. Kenny, D. A., & Campbell, D. T. (1989). On the measurement of stability in over-time data: Special issue: Long-term stability and change in personality. Journal of Personality, 57, 445-481.
40. Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201-210.
41. Kenny, D. A., & Kashy, D. A. (1992). The analysis of the multitrait-multimethod matrix by confirmatory factor analysis. Psychological Bulletin, 112, 165-172.
42. Kenny, D. A., & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63, 52-59.
43. LaFramboise, T. D., Coleman, H. L., & Hernandez, A. (1991). Development and factor structure of the cross-cultural counseling inventory–revised. Professional Psychology: Research and Practice, 22(5), 380-388.
44. Luborsky, L., Crits-Christoph, P., Alexander, L., Margolis, M., & Cohen, M. (1983). Two helping alliance methods of predicting outcomes of psychotherapy. Journal of Nervous and Mental Disease, 171, 480-491.
45. MacCallum, R. C. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107-120.
46. MacCallum, R. C. (1995). Model specification: Procedures, strategies, and related issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 16-36). Thousand Oaks, CA: Sage.
47. MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin, 114, 533-541.
48. MacCallum, R. C., Browne, M., & Sugawara, H. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
49. MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalizing on chance. Psychological Bulletin, 111, 490-504.
50. MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185-199.
51. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519-530.
52. Marcoulides, G. A., & Schumacker, R. E. (Eds.). (1996). Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: Lawrence Erlbaum Associates.
53. Maruyama, G. (1998). Basics of structural equation modeling. Thousand Oaks, CA: Sage.
54. Maxwell, S. E., & Delaney, H. D. (1990). Designing experiments and analyzing data: A model comparison perspective. Pacific Grove: CA: Brooks/Cole.
55. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133.
56. McArdle, J. J., & Hamagami, F. (1996). Multilevel models from a multiple group structural equation perspective. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: Lawrence Erlbaum Associates.
57. McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114, 376-390.
58. Muthén, B. (1984). A general structural equations model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115-132.
59. Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431-462.
60. Nesselroade, J. R. (1991). Interindividual differences in intraindividual change. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 92-105). Washington, DC: American Psychological Association.
61. Pedersen, P. (1991). Multiculturalism as a generic approach to counseling. Journal of Counseling and Development, 70, 6-12.
62. Ping, R. A. (1996). Latent variable interaction and quadratic effect estimation: A two-step technique using structural equation analysis. Psychological Bulletin, 119, 166-175.
63. Ponterotto, J. G. & Alexander, C. M. (1996). Assessing the multicultural compentence of counselors and clinicians. In L. A. Suzuki, P. J. Meller, & J. G. Ponterotto (Eds.), Handbook for multicultural assessment (pp. 651-672). San Francisco: Jossey-Bass.
64. Ponterotto, J. G., Rieger, B. P., Barret, A., & Sparks, R. (1995). Assessing multicultural counseling competence: A review of instrumentation. Journal of Counseling and Development, 72(3), 316-322.
65. Ponterotto, J. G., Sanchez, C. M., & Magids, D. (1991, August). Initial development of the Multicultural Counseling Awareness Scale. Paper presented at the annual meeting of the American Psychological Association, San Francisco.
66. Rencher, A. C. (1995). Methods of multivariate analysis. New York: Wiley.
67. Rice, K. G., Cunningham, T. J., & Young, M. B. (1997). Attachment to parents, social competence, and emotional well-being: A comparison of Black and White adolescents. Journal of Counseling Psychology, 44, 89-101.
68. Russell, D. W., Kahn, J. H., Spoth, R., & Altmaier, E. M. (1998). Analyzing data from experimental studies: A latent variable structural equation modeling approach. Journal of Counseling Psychology, 45, 18-29.
69. Satorra, A., & Bentler, P. M. (1988). Scaling corrections for statistics in covariance structure analysis (UCLA Statistical Series 2). Los Angeles: University of California, Department of Psychology.
70. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistic and standard errors in covariance structure analysis. In A. Von Eye & C. C. Clogg (Eds.), Analysis of latent variables in developmental research (pp. 399-419). Newbury Park, CA: Sage.
71. Schumacker, R. E., & Lomax, R. G. (1996). Beginner–s guide to structural equation modeling. Mahwah, NJ: Lawrence Erlbaum.
72. Serlin, R. C., & Lapsley, D. K. (1985). Rationality in psychological research: The good enough principle. American Psychologist, 40, 73-83.
73. Serlin, R. C., & Lapsley, D. K. (1993). Rational appraisal of psychological research and the good enough principle. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 199-228). Hillsdale, NJ: Erlbaum.
74. Sexton, T. L., & Whiston, S. C. (1994). The status of the counseling relationship: An empirical review, theoretical implications, and research directions. The Counseling Psychologist, 22, 6-78.
75. Steiger, J. H., & Lind, J. C. (1980, June). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.
76. Sue, D. W., Arredondo, P., & McDavis, R. J. (1992). Multicultural competencies and standards: A call to the profession. Journal of Counseling and Development, 70, 477-486.
77. Sue, D. W., Ivey, A. E., & Pedersen, P. B. (Eds.). (1996). Theory of multitcultural counseling and therapy. Pacific Grove, CA: Brooks/Cole.
78. Tanaka, J. S. (1993). Multifaceted conceptions of fit in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equations models (pp. 10-39). Newbury Park, CA: Sage.
79. Utsey, S. O., & Ponterotto, J. G. (1996). Development and validation of the index of race-related stress (IRRS). Journal of Counseling Psychology, 43, 490-501.
80. Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive measures: Nonreactive research in the social sciences. Chicago: Rand McNally.
81. West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). Thousand Oaks, CA: Sage.
82. Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of change. Psychological Bulletin, 116, 363-381.
83. Willett, J. B., & Sayer, A. G. (1996). Cross-domain analyses of change over time: Combining growth modeling and covariance structure analysis. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: Lawrence Erlbaum Associates.