Abstract
The paper describes advances in statistical methods for prevention research with a particular focus on substance abuse prevention. Standard analysis methods are extended to the typical research designs and characteristics of the data collected in prevention research. Prevention research often includes longitudinal measurement, clustering of data in units such as schools or clinics, missing data, and categorical as well as continuous outcome variables. Statistical methods to handle these features of prevention data are outlined. Developments in mediation, moderation, and implementation analysis allow for the extraction of more detailed information from a prevention study. Advancements in the interpretation of prevention research results include more widespread calculation of effect size and statistical power, the use of confidence intervals as well as hypothesis testing, detailed causal analysis of research findings, and meta-analysis. The increased availability of statistical software has contributed greatly to the use of new methods in prevention research. It is likely that the Internet will continue to stimulate the development and application of new methods.
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REFERENCES
Abelson, R. P. (1985). A variance explanation paradox: When a little is a lot. Psychological Bulletin, 97, 129-133.
Abelson, R. P., & Prentice, D. A. (1997). Contrast tests of interaction hypotheses. Psychological Methods, 2, 315-328.
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.
Anderson, E. R. (1995). Accelerating and maximizing information from short-term longitudinal research. In J. M. Gottman (Ed.), The analysis of change. Mahwah, NJ: Erlbaum.
Anselin, L., & Florax, R. J. G. M. (1995). New directions in spatial econometrics. Berlin: Springer.
Arbuckle, J. L. (1997). AMOS [Computer software]. Chicago: Small Waters.
Bacik, J. M., Murphy, S. A., & Anthony, J. C. (1998). Drug use prevention data, missed assessments and survival analysis. Multivariate Behavioral Research, 33, 573-588.
Bailar, J. C., & Mosteller, F. (1988). Guidelines for statistical reporting in articles for medical journals: Amplifications and explanations. Annals of Internal Medicine, 108, 266-273.
Bangert-Drowns, R. L. (1988). The effects of school-based substance abuse education: A meta-analysis. Journal of Drug Education, 18, 243-264.
Barcikowski, R. S. (1981). Statistical power with group mean as the unit of analysis. Journal of Educational Statistics, 6, 267-285.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology, 31, 419-456.
Bentler, P. M. (1997). EQS for Windows (Version 5.6) [Computer software]. Encino, CA: Multivariate Software, Inc.
Bentler, P. M., & Newcomb, M. D. (1986). Personality, sexual behavior, and drug use revealed through latent variable methods. Clinical Psychology Review, 6, 363-385.
Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. In C. C. Clogg (Ed.), Sociological methodology 1987, (pp. 37-69). Washington, DC: American Sociological Association.
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605-634.
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110, 305-314.
Botvin, G. J. (2000). Preventing drug abuse in schools: Social and competence enhancement approaches targeting individual-level etiologic factors. Addictive Behaviors, 25, 887-897.
Brown, C. H., & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging developmental epidemiology paradigm. American Journal of Community Psychology, 27, 673-710.
Brown, H., & Prescott, R. (1999). Applied mixed models in medicine. New York: Wiley.
Brown, C. H., Wang, W., Muthén, B. O., & Dagne, G. (2000). Power calculations on the World Wide Web for longitudinal intervention trials. Technical Report Prevention Science and Methodology Group, Department of Epidemiology and Biostatistics, University of South Florida.
Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62-83.
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models. Applications and data analysis methods. Newbury Park, CA: Sage.
Bryk, A. S., Raudenbush, S. W., Seltzer, M., & Congdon, R. T. (1988). An introduction to HLM: Computer program and user's guide. Chicago: University of Chicago.
Bukoski, W. (Ed.). (1997). Meta-analysis of drug abuse prevention programs. NIDA Research Monograph 170 (NIH Publication No. 97-4146). Rockville, MD: U.S. Department of Health and Human Services.
Chassin, L., Presson, C. C., Pitts, S. C., & Sherman, S. J. (2000). The natural history of cigarette smoking from adolescence to adulthood in a Midwestern community sample: Multiple trajectories and their psychosocial correlates. Health Psychology, 19, 223-231.
Cheong, J., MacKinnon, D. P., & Khoo, S.-T. (2003). Investigation of mediational processes using parallel process latent growth curve modeling. Structural Equation Modeling, 10, 238-262.
Chou, C. P., Montgomery, S., Pentz, M. A., Rohrbach, L. A., Johnson, C. A., Flay, B. R., & MacKinnon, D. P. (1998). Effects of a community-based prevention program on decreasing drug use in high-risk adolescents. American Journal of Public Health, 88, 944-948.
Clogg, C. C. (1995). Latent class models. In G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 311-359). New York: Plenum Press.
Cochrane Controlled Trials Register: In The Cochrane Library, Issue 1 (1999). Oxford: Update Software. (Updated quarterly).
Cohen, J. (1962). The statistical power of abnormal-social psychological research. Journal of Abnormal and Social Psychology, 65, 145-153.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum.
Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304-1312.
Cohen, J., & Cohen, P. (1983). Applied multiple regression/ correlation analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum.
Collins, L. M., & Wugalter, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27, 131-157.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.
Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Orlando, FL: Harcourt, Brace, Jovanovich.
Curran, P. J., Harford, T. C., & Muthén, B. O. (1996). The relation between heavy alcohol use and bar patronage: A latent growth model. Journal of Studies on Alcohol, 57, 410-418.
DiClemente, C. C., Prochaska, J. O., Fairhurst, S. K., Velicer, W. F., Velasquez, M. M., & Rossi, J. S. (1991). The process of smoking cessation, an analysis of precontemplation, contemplation, and preparation stages of change. Journal of Consulting and Clinical Psychology, 59, 295-304.
Diggle, P. J., Liang, K. Y., & Zeger, S. L. (1994). Analysis of longitudinal data. Oxford: Clarendon Press.
Donaldson, S. I., Graham, J. W., & Hanson, W. B. (1994). Testing the generalizability of intervening mechanism theories: Understanding the effects of adolescent drug use prevention interventions. Journal of Behavioral Medicine, 17, 195-216.
Donner, A. (1985). A regression approach to the analysis of data arising from cluster randomization. International Journal of Epidemiology, 14, 322-326.
Duncan, T. E., & Duncan, S. C. (1996). A multivariate latent growth curve analysis of adolescent substance use. Structural Equation Modeling, 3, 323-347.
Duncan, T. E., Duncan, S. C., & Hops, H. (1996). Analysis of longitudinal data within accelerated longitudinal designs. Psychological Methods, 1, 236-248.
Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Erlbaum.
Edgington, E. S. (1995). Randomization tests. New York: Marcel Dekker.
Efron, B. (2000). The bootstrap and modern statistics. Journal of the American Statistical Association, 95, 1293-1296.
Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. New York: Chapman and Hall.
EGRET [Computer software]. (1991). Seattle: Statistics and Epidemiology Research Corporation.
Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8, 128-141.
Freiman, J. A., Chalmers, T. C., Smith, H., & Kuebler, R. R. (1978). The importance of beta, the type 2 error and sample size in the design and interpretation of the randomized control trial. The New England Journal of Medicine, 299, 690-694.
Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5, 3-8.
Goldstein, R. (1989). Power and sample size via MS/PC-DOS computers. American Statistician, 43, 253-260.
Goldstein, H. (1991). Nonlinear multilevel models with an application to discrete response data. Biometrika, 78, 45-51.
Goldstein, H., & Rasbash, J. (1996). Improved approximations for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A, 159, 505-513.
Goldstein, H., Rasbash, J., Plewis, I., Draper, D., Browne, W., Yang, M., Woodhouse, G., & Healy, M. (1998). A user's guide to MlwiN. London: University of London.
Graham, J. W., & Donaldson, S. I. (1993). Evaluating interventions with differential attrition: The importance of nonresponse mechanisms and use of follow-up data. Journal of Applied Psychology, 78, 119-128.
Graham, J. W., Hofer, S. M., Donaldson, S. I., MacKinnon, D. P., & Schafer, J. L. (1997). Analysis with missing data in prevention research. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 325-365). Washington, DC: American Psychological Association.
Graham, J. W., Hofer, S. M., & MacKinnon, D. P. (1996). Maximizing the usefulness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behavioral Research, 31, 197-218.
Graham, J. W., Rohrbach, L. A., Hansen, W. B., Flay, B. R., & Johnson, C. A. (1989). Convergent and discriminant validity for assessment of skill in resisting a role play alcohol offer. Behavioral Assessment, 11, 353-379.
Grover, P. L. (1999).Preventing problems related to alcohol availability: Environmental approaches (DHHS Publication No. (SMA) 99-3298). Rockville, MD, Department of Health and Human Services.
Gruenewald, P. J., Millar, A. B., Treno, A. J., Yang, Z., Ponicki, W. R., & Roeper, P. (1996). The geography of availability and driving after drinking. Addiction, 91, 967-983.
Haggard, E. A. (1958). Intraclass correlation and the analysis of variance. New York: Dryden Press.
Hansen, W. B. (1992). School-based substance abuse prevention: Review of the state of the art in curriculum, 1980-1990. Health Education Research, 7, 403-430.
Hansen, W. B., Tobler, N. S., & Graham, J. W. (1990). Attrition in substance abuse prevention research: A meta-analysis of 85 longitudinally followed cohorts. Evaluation Review, 14, 677-685.
Harlow, L. L. (1986). Behavior of some elliptical theory estimators with nonnormal data in a covariance structures framework: A Monte Carlo study. Unpublished doctoral dissertation, University of California, Los Angeles.
Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.). (1997) What if there were no significance tests? Mahwah, NJ: Erlbaum.
Harrison, L., & Hughes, A. (1997). The validity of self-reported drug use: Improving the accuracy of survey estimates. In The validity of self-reported drug use: Improving the accuracy of survey estimates, NIDA research monograph 167 (DHHS Publication No. 97-4147, pp. 1-16). Rockville, MD: U.S. Department of Health and Human Services.
Hedeker, D., & Gibbons, R. D. (1996). MIXREG: A computer program for mixed-effects regression analysis with autocorrelated errors. Computer Methods and Programs in Biomedicine, 49, 229-252.
Hedeker, D., & Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78.
Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. In C. Clogg (Ed.), Sociological methodology, 1988 (pp. 449-484). Washington, DC: American Sociological Association.
Hosmer, D. W., & Lemeshow, S. (1989). Applied logistic regression analysis. New York: Wiley.
Hosmer, D. W., & Lemeshow, S. (1999). Applied survival analysis: Regression modeling of time to event data. New York: Wiley.
Hsieh, F. Y. (1989). Sample size tables for logistic regression. Statistics in Medicine, 8, 795-802.
Huba, G. J., & Bentler, P. M. (1983). Test of a drug use causal model using asymptotically distribution free methods. Journal of Drug Education, 13, 3-14.
Hyatt, S. L., & Collins, L. M. (2000). Using latent transition analysis to examine the relationship between perceived parental permissiveness and the onset of substance use. In J. S. Rose, L. Chassin, C. C. Presson, & S. J. Sherman (Eds.), Multivariate applications in substance use research (pp. 141-160). New York: Erlbaum.
Jones, B. L., Nagin, D. S., & Roeder, K. (2001, May). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods and Research, 29, 374-393.
Jöreskog, K., & Sörbom, D. (2000). LISREL [Computer software]. Lincolnwood, IL: Scientific Software, Inc.
Judd, C. M., & Kenny, D. A. (1981). Estimating the effects of social interventions. New York: Cambridge University Press.
Kandel, D. B. (1991). The social demography of drug use. Milbank Quarterly, 69, 365-414.
Kaplan, D. (1995). Statistical power in structural equation modeling. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 100-117). Newbury Park, CA: Sage.
Keppel, G. (1991). Design and analysis: A researcher's handbook. Englewood Cliffs, NJ: Prentice-Hall.
Kirk, R. E. (1995). Experimental design: Procedures for the behavioral sciences. Pacific Grove, CA: Brooks/Cole.
Kolbe, L. J. (1986). Increasing the impact of school health promotion programs: Emerging research perspectives. Health Education, 17, 47-52.
Kosterman, R., Hawkins, J. D., Guo, J., Catalano, R. F., & Abbott, R. D. (2000). The dynamics of alcohol and marijuana initiation: Patterns and predictors of first use in adolescence. American Journal of Public Health, 90, 360-366.
Krantz, D. H. (1999). The null hypothesis testing controversy in psychology. Journal of the American Statistical Association, 44, 1372-1381.
Krull, J. L., & MacKinnon, D. P. (1999). Multilevel mediation modeling in group-based intervention studies. Evaluation Review, 23, 418-444.
Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research, 36, 249-277.
Laub, J. H., Nagin, D. S., & Sampson, R. J. (1998). Trajectories of change in criminal offending: Good marriages and the resistance process. American Sociological Review, 63, 225-238.
Littell, R. C., Milliken, G. A., Stroup, W. W., & Wolfinger, R. D. (1996). SAS system for mixed models. Cary, NC: SAS Institute.
Little, T. D., Schnabel, K. U., & Baumert, J. (2000). Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples, Mahwah, NJ: Erlbaum.
Little, R., & Yau, L. (1996). Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics, 52, 1324-1333.
Little, R. J., & Yau, L. H. Y. (1998). Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model. Psychological Methods, 3, 147-159.
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin, 114, 533-541.
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.
MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In J. S. Rose, L. Chassin, C. C. Presson, & S. J. Sherman (Eds.), Multivariate applications in substance use research (pp. 141-160). New York: Erlbaum.
MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144-158.
MacKinnon, D. P., Johnson, C. A., Pentz, M. A., Dwyer, J. H., Hansen, W. B., Flay, B. R., & Wang, E. Y. (1991). Mediating mechanisms in a school-based drug prevention program: First-year effects of the Midwestern Prevention Project. Health Psychology, 10, 164-172.
Malakoff, D. (1999). Bayes offers a new way to make sense of numbers. Science, 286, 1460-1464.
Manly, B. F. J. (1998). Randomization, bootstrap and Monte Carlo methods in biology. London: Chapman & Hall.
Millsap, R. E. (1995). Measurement invariance, predictive invariance, and the duality paradox. Multivariate Behavioral Research, 30, 577-605.
Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Newbury Park, CA: Sage.
Murray, D. M. (1998). Design and analysis of group-randomized trials. New York: Oxford University Press.
Murray, D. M., Rooney, B. L., Hannan, P. J., Peterson, A. V., Ary, D. V., Biglan, A., Botvin, G. J., Evans, R. I., Flay, B. R., Futterman, R., Getz, J. G., Marek, P. M., Orlandi, M., Pentz, M. A., Perry, C. L., & Schinke, S. P. (1994). Intraclass correlation among common measures of adolescent smoking: Estimates, correlates, and applications in smoking prevention studies. American Journal of Epidemiology, 140, 1038-1050.
Muthén, B. O. (1998, October). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. Paper presented at the conference of New Methods for the Analysis of Change, Pennsylvania State University, PA.
Muthén, B. O., & Curran, P. J. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.
Muthén, B. O., & Khoo, S.-T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, 10, 73-101.
Muthén, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
Muthén, L. K., & Muthén B. O. (1998). MPlus [Computer software]. Mooresville, IN: Scientific Software.
Nagin, D. S., Farrington, D., & Moffit, T. (1995). Life-course trajectories of different types of offenders. Criminology, 33, 111-139.
National Center for Health Statistics (2000). Current legislative authorities of the national center for health statistics. Hyattsville, MD: U.S. Department of Health and Human Services, Center for Disease Control and Prevention.
Newcomb, M. D., & Bentler, P. M. (1988). Consequences of adolescent drug use: Impact on the lives of young adults. Newbury Park, CA: Sage.
Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241-301.
NIH Program Announcement (1995). Secondary analysis of alcohol abuse prevention research data (NIH Guide, Vol. 24., No. 4., PAR-95-024). Rockville, MD.
Noreen, E. W. (1989). Computer-intensive methods for testing hypotheses: An introduction. New York: John Wiley.
Palmer, R. F., Dwyer, J. H., & Semmer, N. (1994). A measurement model of adolescent smoking. Addictive Behaviors, 19, 477-489.
Palmer, R. F., Graham, J. W., White, E. L., & Hansen, W. B. (1998). Applying multilevel analytic strategies in adolescent substance use prevention research. Preventive Medicine, 27, 328-336.
Pentz, M. A., & Trebow, E. (1991). Implementation issues in drug abuse prevention research. In C. G. Leukefeld & W. J. Bukoski (Eds.), Drug abuse prevention intervention research: Methodological issues, NIDA Research Monograph 107 (DHHS Publication No. 91-1761, pp. 123-139). Washington, DC: U.S. Department of Health and Human Services.
Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143-155.
Rogosa, D. (1988). Myths about longitudinal research. In K. W. Schaie, R. T. Campbell, W. Meredith, & S. C. Rawlings (Eds.), Methodological issues in aging research (pp. 171-209). New York: Springer.
Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44, 1276-1284.
Rossi, J. S. (1990). Statistical power of psychological research: What have we gained in 20 years? Journal of Consulting and Clinical Psychology, 58, 646-656.
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701.
Sambrano, S. (1996). Center for Substance Abuse Prevention national cross-site evaluation of high risk youth programs: Research design. St. Louis, MO: EMT Associates.
Sambrano, S., Springer, J. F., Hermann, J. (1997). Informing the next generation of prevention programs: CSAP'S cross-site evaluation of the 1994-1995 high risk youth grantees. Journal of Community Psychology, 25, 375-395.
SAS (Version 8.0) [Computer software]. (1999). Cary, NC: SAS Institute, Inc.
SAS Institute, Inc. (1992). The MIXED procedure. In SAS® technical report P-229, SAS/STAT® Software: Changes and enhancements, release 6.07 (pp. 287-366). Cary, NC: SAS Institute, Inc.
SAS Institute, Inc. (1997). The GENMOD Procedure. In SAS/STAT® Software: Changes and enhancements through release 6.12 (pp. 247-348). Cary, NC: SAS Institute, Inc.
Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83-90.
Schumacker, R. E., & Marcoulides, G. A. (1998). Interaction and nonlinear effects in structural equation modeling. Mahwah, NJ: Erlbaum.
Selvin, S. (1996). Statistical analysis of epidemiologic data. Oxford: Oxford University Press.
Shafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall/CRC Press.
Siddiqui, O., Flay, B. R., & Hu, F. B. (1996). Factors affecting attrition in longitudinal smoking prevention study. Preventive Medicine, 25(5), 554-560.
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 23, 323-355.
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. New York: Springer-Verlag.
SPSS (Version 9.0) [Computer software]. (1999). Chicago: SPSS, Inc.
Stoolmiller, M. (1994). Antisocial behavior, delinquent peer association, and unsupervised wandering for boys: Growth and change from childhood to early adolescence. Multivariate Behavioral Research, 29, 263-288.
Thomas, L., & Kreb, C. J. (1997). A review of statistical power analysis software. Bulletin of the Ecological Society of America, 78, 126-139.
Tobler, N. S. (1986). Meta-analysis of 143 adolescent drug prevention programs: Quantitative outcome results of program participants compared to a control or comparison group. Journal of Drug Issues, 16, 537-567.
Verbeke, G., & Lesaffre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. Journal of the American Statistical Association, 91, 217-221.
Voas, R. B., Marques, P. R., Tippetts, A. S., & Beirness, D. J. (1999). The Alberta interlock program: The evaluation of a province-wide program on DUI recidivism. Addiction, 94, 1857-1867.
West, S. G., & Aiken, L. S. (1997). Toward understanding individual effects in multicomponent prevention programs: Design and analysis strategies. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 167-209). Washington, DC: American Psychological Association.
Widaman, K. F., & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281-324). Washington, DC: American Psychological Association.
Wilkinson, L., & the Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.
Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363-381.
Willett, J. B., Singer, J. D., & Martin, N. C. (1998). The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations. Development and Psychopathology, 10, 395-426.
Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659-707.
Yin, R. K., & Kaftarian, S. J. (1997). Introduction: Challenges of community-based program outcome evaluations. Evaluation and Program Planning, 20, 293-197.
Yin, R. K., Kaftarian, S. J., Yu, P., & Jansen, M. A. (1997). Outcomes from CSAP's community partnership program: Findings from the national cross-site evaluation. Evaluation and Program Planning, 20, 345-355.
Yuan, K. H., & Bentler, P. M. (1997). Mean and covariance structure analysis: Theoretical and practical improvements. Journal of the American Statistical Association, 92, 767-774.
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MacKinnon, D.P., Lockwood, C.M. Advances in Statistical Methods for Substance Abuse Prevention Research. Prev Sci 4, 155–171 (2003). https://doi.org/10.1023/A:1024649822872
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DOI: https://doi.org/10.1023/A:1024649822872