A regression framework for effect size assessments in longitudinal modeling of group differences

Review of General Psychology

Volumn 17, Issue 1, 2013, Pages 111-121

  Feingold, Alan ^a  

^a OREGON SOCIAL LEARNING CENTER   (United States)

Author keywords

Clinical trials; Effect sizes; Multilevel analysis; Regression

Indexed keywords

EID: 84881245980     PISSN: 10892680     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0030048     Document Type: Article

References (64)

1. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York, NY: Wiley. doi:10.1002/0471249688
2. Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America. American Psychologist, 63, 32-50. doi:10.1037/0003-066X.63.1.32
3. APA Publications and Communications Board Working Group on Journal Article Reporting Standards. (2008). Reporting standards for research in psychology: Why do we need them? What might they be? American Psychologist, 63, 839-851. doi:10.1037/0003-066X.63.9.839
4. Atkins, D. C. (2005). Using multilevel models to analyze couple and family treatment data: Basic and advanced issues. Journal of Family Psychology, 19, 98-110. doi:10.1037/0893-3200.19.1.98
5. Babor, T. F., & Del Boca, F. K. (Eds.). (2003). Treatment matching in alcoholism. New York, NY: Cambridge University Press.
6. Becker, B. J. (1988). Synthesizing standardized mean change scores. British Journal of Mathematical and Statistical Psychology, 41, 257-278. doi:10.1111/j.2044-8317.1988.tb00901.x
7. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. New York, NY: Wiley. doi:10.1002/ 9780470743386
8. Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine, 19, 3127-3131. doi:10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0 .CO;2-M
9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
10. Cohen, P., Cohen, J., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral analysis (3rd ed.). Mahwah, NJ: Erlbaum.
11. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis. Hoboken, NJ: Wiley.
12. Cooper, H. M. (2010). Research synthesis and meta-analysis ((4th ed.). Los Angeles, CA: Sage.
13. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1-38.
14. Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. Psychological Methods, 1, 170-177. doi:10.1037/1082-989X.1 .2.170
15. Eagly, A. H. (1995). The science and politics of comparing women and men. American Psychologist, 50, 145-158. doi:10.1037/0003-066X.50 .3.145
16. Fairchild, A. J., MacKinnon, D. P., Taborga, M. P., & Taylor, A. B. (2009). R2 effect-size measures for mediation analysis. Behavior Research Methods, 41, 486-498. doi:10.3758/BRM.41.2.486
17. Feingold, A. (1994). Gender differences in personality: A meta-analysis. Psychological Bulletin, 116, 429-456. doi:10.1037/0033-2909.116.3 .429
18. Feingold, A. (2009). Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis. Psychological Methods, 14, 43-53. doi:10.1037/a0014699
19. Feingold, A., Ball, S. A., Kranzler, H. R., & Rounsaville, B. J. (1996). Generalizability of the Type A/Type B distinction across different psychoactive substances. The American Journal of Drug and Alcohol Abuse, 22, 449-462. doi:10.3109/00952999609001671
20. Feingold, A., Kerr, D. C. R., & Capaldi, D. M. (2008). Associations of substance use problems with intimate partner violence in long-term relationships. Journal of Family Psychology, 22, 429-438. doi:10.1037/ 0893-3200.22.3.429
21. Fleiss, J. L., & Berlin, J. A. (2009). Effect sizes for dichotomous data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis (2nd ed., pp. 237-253). New York, NY: Russell Sage.
22. Gibbons, R. D., Hedeker, D., Elkin, I., Waternaux, C. M., Kraemer, H. C., Greenhouse, J. B., . . . Watkins, J. T. (1993). Some conceptual and statistical issues in analysis of longitudinal psychiatric data. Archives of General Psychiatry, 50, 739-750. doi:10.1001/archpsyc.1993 .01820210073009
23. Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis in social research. Thousand Oaks, CA: Sage.
24. Grissom, R. J., & Kim, J. J. (2005). Effect sizes for research: A broad practical approach. Hillsdale, NJ: Erlbaum.
25. Gueorguieva, R., & Krystal, J. H. (2004). Move over ANOVA: Progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry. Archives of General Psychiatry, 61, 310-317. doi:10.1001/archpsyc.61.3.310
26. Haddock, C. K., Rindskopf, D., & Shadish, W. R. (1998). Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues. Psychological Methods, 3, 339-353. doi:10.1037/ 1082-989X.3.3.339
27. Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. Hoboken, NJ: Wiley.
28. Hedges, L. V. (2007). Effect sizes in cluster-randomized designs. Journal of Educational and Behavioral Statistics, 32, 341-370. doi:10.3102/ 1076998606298043
29. Hedges, L. V. (2009). Effect sizes in nested designs. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis (2nd ed., pp. 337-356). New York, NY: Russell Sage.
30. Hettema, J. E., & Hendricks, P. S. (2010). Motivational interviewing for smoking cessation: A meta-analytic review. Journal of Consulting and Clinical Psychology, 78, 868-884. doi:10.1037/a0021498
31. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York, NY: Wiley. doi:10.1002/0471722146
32. Huttenlocher, J., Haight, W., Bryk, A., & Seltzer, M. (1991). Early vocabulary growth: Relation to language input and gender. Developmental Psychology, 27, 236-248. doi:10.1037/0012-1649.27.2.236
33. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581-592. doi:10.1037/0003-066X.60.6.581
34. Jensen, C. D., Cushing, C. C., Aylward, B. S., Craig, J. T., Sorell, D. M., & Steele, R. G. (2011). Effectiveness of motivational interviewing interventions for adolescent substance use behavior change: A metaanalytic review. Journal of Consulting and Clinical Psychology, 79, 433-440. doi:10.1037/a0023992
35. Kuljanin, G., Braun, M. T., & DeShon, R. P. (2011). A cautionary note on modeling growth trends in longitudinal data. Psychological Methods, 16, 249-264. doi:10.1037/a0023348
36. Leahey, E., & Guo, G. (2001). Gender differences in mathematical trajectories. Social Forces, 80, 713-732. doi:10.1353/sof.2001.0102
37. Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.
38. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York, NY: Wiley.
39. McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models. New York, NY: Wiley.
40. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122. doi:10.1007/BF02294746
41. Morris, S. B. (2008). Estimating effect sizes from pretest-posttest-control group designs. Organizational Research Methods, 11, 364-386. doi: 10.1177/1094428106291059
42. Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological Methods, 7, 105-125. doi:10.1037/1082-989X.7.1.105
43. Muthén, B., Brown, C. H., Masyn, K., Jo, B., Khoo, S.-T., Yang, C.-C., . . . Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475. doi:10.1093/ biostatistics/3.4.459
44. Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599-620. doi:10.1207/S15328007SEM0904_8
45. Muthén, L. K., & Muthén, B. O. (2010). Mplus user's guide (6th ed.). Los Angeles, CA: Author.
46. Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.
47. Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370-384. doi: 10.2307/2344614
48. Nemes, S., Jonasson, M. J., Genell, A., & Steineck, G. (2009). Bias in odds ratios by logistic regression modelling and sample size. BMC Medical Research Methodology, 9, 56-61. doi:10.1186/1471-2288-9-56
49. Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8, 434-447. doi:10.1037/1082-989X.8.4.434
50. Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16, 93-115. doi:10.1037/a0022658
51. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
52. Raudenbush, S., Bryk, A., Cheong, Y., Congdon, R., & du Toit, M. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.
53. Raudenbush, S. W., & Liu, X. (2001). Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychological Methods, 6, 387-401. doi: 10.1037/1082-989X.6.4.387
54. Roberts, A., Cash, T. F., Feingold, A., & Johnson, B. T. (2006). Are Black-White differences in females' body dissatisfaction decreasing? A meta-analytic review. Journal of Consulting and Clinical Psychology, 74, 1121-1131.
55. Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000). Contrasts and effect sizes in behavioral research: A correlational approach. Cambridge, England: Cambridge University Press.
56. Sánchez-Meca, J., Marín-Martínez, F., & Chacón-Moscoso, S. (2003). Effect-size indices for dichotomized outcomes in meta-analysis. Psychological Methods, 8, 448-467. doi:10.1037/1082-989X.8.4.448
57. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177. doi:10.1037/1082-989X .7.2.147
58. Schmidt, F. L., Lee, H., & Oh, I. (2009). Correcting for the distorting effects of study artifacts in meta-analysis. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis (2nd ed., pp. 317-333). New York: Russell Sage.
59. Shadish, W. R., & Haddock, C. K. (2009). Combining estimates of effect sizes. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis (2nd ed., pp. 257-277). New York, NY: Russell Sage.
60. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.
61. Steinley, D., & Brusco, M. J. (2011). Choosing the number of clusters in K-means clustering. Psychological Methods, 16, 285-297. doi:10.1037/ a0023346
62. Supplee, L. H. (Ed.). (2008). The application of effect sizes in research on children and families [Special section]. Child Development Perspectives, 2, 164-166. doi:10.1111/j.1750-8606.2008.00059.x
63. Winer, B. J. (1971). Statistical principles in experimental design (2nd ed.). New York, NY: McGraw-Hill.
64. Wright, D. B. (2009). Ten statisticians and their impacts for psychologists. Perspectives on Psychological Science, 4, 587-597. doi:10.1111/j.1745- 6924.2009.01167.x