Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T16:08:17.466Z Has data issue: false hasContentIssue false

9 - Methodology and Statistical Approaches for Conducting Valid and Reliable Longitudinal Prevention Science Research

from Methodology

Published online by Cambridge University Press:  21 January 2017

By
Jenn-Yun Tein
Edited by
Moshe Israelashvili  and
John L. Romano
Moshe Israelashvili
Affiliation:
Tel-Aviv University
John L. Romano
Affiliation:
University of Minnesota
Get access
Type
Chapter
Information
The Cambridge Handbook of International Prevention Science , pp. 179 - 205
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achenbach, T. M., McConaughy, S. H., & Howell, C. T. (1987). Child/adolescent behavioral and emotional problems: implications of cross-informant correlations for situational specificity. Psychological Bulletin 101: 213–32. doi.org/10.1037/0033–2909.101.2.213CrossRefGoogle ScholarPubMed
Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: a 30-year review. Journal of Applied Psychology 90: 94–107. doi: 10.1037/0021–9010.90.1.94CrossRefGoogle ScholarPubMed
Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.Google Scholar
Albert, J. M. (2008). Mediation analysis via potential outcomes models. Statistics in Medicine 27(8): 1282–1304. doi: 10.1002/sim.3016CrossRefGoogle ScholarPubMed
Angrist, J., Imbens, G., & Rubin, D. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91: 444–72. doi: 10.1080/01621459.1996.10476902Google Scholar
Arbuckle, J. L. (2006). Amos 7.0 User’s Guide. Chicago, IL: SPSS.Google Scholar
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: 1173–82. doi.org/10.1037/0022–3514.51.6.1173Google Scholar
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin 107: 238–46. doi.org/10.1037/0033–2909.107.2.238Google Scholar
Bentler, P. M. (2006). EQS 6 Structural Equation Program Manual. Encino, CA: Multivariate Software, Inc.Google Scholar
Blankson, A. N., & McArdle, J. J. (2013). Measurement invariance of cognitive abilities across ethnicity, gender, and time among older Americans. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences 70: 386–97. doi: 10.1093/geronb/gbt106Google ScholarPubMed
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. doi.org/10.1023/A:1022142021441CrossRefGoogle ScholarPubMed
Brown, C. H., Wang, W., Kellam, S. G., Muthén, M. O., Petras, H., Topinbo, P., … et al., Prevention Science and Methodology Group (2008). Methods for testing theory and evaluating Impact in randomized field trials: intent-to-treat analyses for integrating the perspectives of person, place, and time. Journal of Drug and Alcohol Dependence 95(Supplement 1): s74–s104. doi.org/10.1016/j.drugalcdep.2007.11.013CrossRefGoogle ScholarPubMed
Cázares, A., & Beatty, L. A. (eds.) (1994). Scientific Methods for Prevention Intervention Research. Rockville, MD: National Institute on Drug Abuse Research Monograph.Google Scholar
Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research 41: 189–225. doi.org/10.1207/s15327906mbr4102_5CrossRefGoogle ScholarPubMed
Chen, H.-T. (1990). Theory-Driven Evaluations. Newbury Park, CA: Sage.Google Scholar
Coertjens, L., Donche, V., De Maeyer, S., Vanthournout, G., & Van Petegem, P. (2012). Longitudinal measurement invariance of Likert-type learning strategy scales: are we using the same ruler at each wave? Journal of Psychoeducational Assessment 30: 577–87. doi.org/10.1177/0734282912438844Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Connell, A. (2009). Employing complier average causal effect analytic methods to examine effects of randomized encouragement trials. American Journal of Drug and Alcohol Abuse 35: 253–59. doi: 10.1080/00952990903005882CrossRefGoogle ScholarPubMed
Cotter, R. B., Burke, J. D., Loeber, R,, & Navratil, J. L. (2002). Innovative retention methods in longitudinal research: a case study of the developmental trends study. Journal of Child and Family Studies 11: 485–98. doi: 10.1023/A:1020939626243CrossRefGoogle Scholar
Cronbach, L. J., Ambron, S. R., Dornbursch, S. M., Hess, R. D., Hornik, R. C., Phillips, D. C., … Weiner, S. S. (1980). Toward Reform of Program Evaluation. San Francisco, CA: Jossey-Bass.Google Scholar
Dane, A. V., & Schneider, B. H. (1998). Program integrity in primary and early secondary prevention: are implementation effects out of control? Clinical Psychology Review 18: 23–45. doi.org/10.1016/S0272-7358(97)00043–3CrossRefGoogle ScholarPubMed
De Los Reyes, A., Thomas, S. A., Goodman, K. L., & Kundey, S. M. A. (2013). Principles underlying the use of multiple informants’ reports. Annual Review of Clinical Psychology 9: 123–49. doi: 10.1146/annurev-clinpsy-050212–185617Google Scholar
Demidenko, E. (2007). Sample size determination for logistic regression revisited. Statistics in Medicine 26: 3385–97. doi: 10.1002/sim.2771CrossRefGoogle ScholarPubMed
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B 39: 1–38.Google Scholar
Diggle, P. J., & Kenward, M. G. (1994). Informative dropout in longitudinal data analysis (with discussion). Applied Statistics 43: 49–73. doi.org/10.2307/2986113Google Scholar
Durlak, J. A. & DuPre, E. P. (2008). Implementation matters: a review of research on the influence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology 41: 327–50. doi.org/10.1007/s10464-008–9165-0CrossRefGoogle ScholarPubMed
Elwood, P. C. (1982). Randomised controlled trials: sampling, British Journal of Clinical Pharmacology 13: 631–6. doi: 10.1111/j.1365–2125.1982.tb01429.xCrossRefGoogle ScholarPubMed
Enders, C. K. (n. d.). Applied missing data.com: companion website for applied missing data analysis. www.appliedmissingdata.com/macro-programs.html.Google Scholar
Enders, C. K. (2011). Missing not at random models for latent growth curve analyses. Psychological Methods 16: 1–16. doi.org/10.1037/a0022640CrossRefGoogle Scholar
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2009). Statistical power analyses using G * Power 3.1: tests for correlation and regression analyses. Behavior Research Methods 41: 1149–60. doi: 10.3758/BRM.41.4.1149CrossRefGoogle ScholarPubMed
Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science 18: 233–9. doi.org/10.1111/j.1467–9280.2007.01882.xGoogle Scholar
Hansen, W. B., Collin, L. M., Malotte, C. K., Johnson, C. A., & Fielding, J. E. (1985). Attrition in prevention research. Journal of Behavioral Medicine 8: 261–75. doi.org/10.1007/BF00870313Google Scholar
Hedeker, D., & Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods 2: 64–78. doi.org/10.1037/1082–989X.2.1.64Google Scholar
Hedges, L. V., & Hedberg, E. C. (2007). Intraclass correlations for planning group-randomized experiments in education. Educational Evaluation and Policy Analysis 29: 60–87.CrossRefGoogle Scholar
Hedges, L. V., & Rhoads, C. (2009). Statistical Power Analysis in Education Research (NCSER 2010–3006). Washington, DC: National Center for Special Education Research, Institute of Education Sciences, U.S. Department of Education.Google Scholar
Hox, J. (2010). Multilevel Analysis: Techniques and Applications, 2nd ed. New York: Routledge.Google Scholar
Hsieh, F. Y., Bloch, D. A., & Larsen, M. D. (1998). A simple method of sample size calculation for linear and logistic regression. Statistics in Medicine 17: 1623–34. doi.org/10.1002/(SICI)1097-0258(19980730)17:14%3C1623::AID-SIM871%3E3.0.CO;2-SGoogle Scholar
Hui, C. H., & Triandis, H. C. (1985). Measurement in cross-cultural psychology: a review and comparison of strategies. Journal of Cross-Cultural Psychology 16: 131–52. doi: 1177/0022002186017002006Google Scholar
Hunsley, J., & Mash, E. J. (2007). Evidence-based assessment. Annual Review of Clinical Psychology 3: 29–51. doi.org/10.1146/annurev.clinpsy.3.022806.091419CrossRefGoogle ScholarPubMed
IBM Corp. (2013). IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.Google Scholar
Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods 15: 309–34. doi.org/10.1037/a0020761CrossRefGoogle ScholarPubMed
Jo, B. (2002). Estimating intervention effects with noncompliance: alternative model specifications. Journal of Educational and Behavioral Statistics 27: 385–420. doi.org/10.3102/10769986027004385Google Scholar
Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods 13: 314–36. doi.org/10.1037/a0014207Google Scholar
Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research 29: 374–93.Google Scholar
Jöreskog, K. G. & Sörbom, D. (1988). LISREL 7 – a Guide to the Program and Applications, 2nd ed. Chicago, IL: SPSS.Google Scholar
Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.8 for Windows [computer software]. Skokie, IL: Scientific Software International, Inc.Google Scholar
Jurs, S. G., & Glass, G.V. (1971). The effect of experimental mortality on the internal and external validity of the randomized comparative experiment. Journal of Experimental Education 40: 62–6. doi: 10.1080/00220973.1971.11011304Google Scholar
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001). Analyzing incomplete political science data: an alternative algorithm for multiple imputation. American Political Science Review 95: 49–69.CrossRefGoogle Scholar
Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001). How do risk factors work together? Mediators, moderators, and independent, overlapping and proxy risk factors. American Journal of Psychiatry 158: 848–83. doi.org/10.1176/appi.ajp.158.6.848Google Scholar
Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry 59: 877–84. doi.org/10.1001/archpsyc.59.10.877Google Scholar
Kwok, O., Haine, R. A., Sandler, I. N., Ayers, T. S., Wolchik, S. A., & Tein, J.-Y. (2005). Positive parenting as a mediator of the relations between parental psychological distress and mental health problems of parentally bereaved children. Journal of Clinical Child and Adolescent Psychology 34: 260–71. doi: 10.1207/s15374424jccp3402_5Google Scholar
Lipsey, M. W. (1990). Design Sensitivity: Statistical Power for Experimental Research. Newbury Park, CA: Sage.Google Scholar
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association 83: 1198–1202. doi.org/10.1080/01621459.1988.10478722Google Scholar
Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88: 125–34. doi.org/10.1080/01621459.1993.10594302Google Scholar
Little, R. J. A. (1995). Modeling the dropout mechanism in repeated-measures studies. Journal of the American Statistical Association 90: 1112–21. doi.org/10.1080/01621459.1995.10476615Google Scholar
Little, R. J. A., & Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. New York: John Wiley & Sons.CrossRefGoogle Scholar
Liu, Y., Millsap, R., West, S. G., Tein, J.-Y., Tanaka, R., & Grimm, K. J. (in press). Testing measurement invariance in longitudinal data with ordered-categorical measures. Psychological Methods.Google Scholar
Lynch, K. G., Cary, M., Gallop, R., & Ten Have, T. (2008). Causal mediation analyses for randomized trials. Health Services and Outcomes Research Methodology 8: 57–76. doi: 10.1007/s10742-008-0028-9Google Scholar
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods 7: 19–40. doi: 10.1037//1082–989X.7.1.19Google Scholar
MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. New York: Taylor & Francis Group.Google Scholar
MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding, and suppression effect. Prevention Science 1: 173–81. doi.org/10.1023/A:1026595011371CrossRefGoogle ScholarPubMed
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test the significance of the mediated effect. Psychological Methods 7: 83–104. doi.org/10.1037/1082–989X.7.1.83Google Scholar
MacKinnon, D. P., Taborga, M. P., & Morgan-Lopez, A. A. (2002). Mediation designs for tobacco prevention research. Journal of Drug and Alcohol Dependence 68(Supplement 1): s74–s104. doi.org/10.1016/S0376-8716(02)00216–8Google Scholar
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods 12: 23–44. doi.org/10.1037/1082–989X.12.1.23Google Scholar
McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin 114: 376–90. doi.org/10.1037/0033–2909.114.2.376Google Scholar
McKay, M. M., & Bannon, W. M. J. (2004). Engaging families in child mental health services. Child and Adolescent Psychiatric Clinics of North America 13: 905–21. doi: 10.1016/j.chc.2004.04.001Google Scholar
Milfont, T. L., & Fischer, R. (2010). Testing measurement invariance across groups: application in cross-cultural research. International Journal of Psychological Research 3: 111–21.Google Scholar
Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. New York: Routledge.Google Scholar
Millsap, R. E., & Tein, J.-Y. (2004). Assessing factorial invariance in ordered-categorical measures. Multivariate Behavioral Research 39: 479–515. doi: 10.1207/S15327906MBR3903_4CrossRefGoogle Scholar
Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gøtzsche, P. C., Devereaux, P. J., Elbourne, D., Egger, M., & Altman, D. G. (2010). CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. Journal of Clinical Epidemiology 63: e1–e37. doi: 10.1016/j.jclinepi.2010.03.004Google Scholar
Múthen, B. O., Brown, C. H., Masyn, K., Jo, B., Khoo, S., Yang, C.-C., Wang, C.-P, Kellam, S., Carlin, J. B., & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics 3: 459–75. doi.org/10.1093/biostatistics/3.4.459Google Scholar
Múthen, 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. doi.org/10.1037/1082–989X.2.4.371Google Scholar
Muthén, L. K., & Muthén, B. O. (1998–2014). Mplus User’s Guide, 7th ed. Los Angeles, CA: Muthén & Muthén.Google Scholar
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.org/10.1207/S15328007SEM0904_8Google Scholar
Nagin, D. S. (1999). Analyzing developmental trajectories: a semiparametric group-based approach. Psychological Methods 4: 139–57. doi.org/10.1037/1082–989X.4.2.139Google Scholar
National Research Council and Institute of Medicine (2009). Preventing Mental, Emotional, and Behavioral Disorders among Young People: Progress and Possibilities. Committee on the Prevention of Mental Disorders and Substance Abuse among Children, Youth, and Young Adults: Research Advances and Promising Interventions. O’Connell, M. E., Boat, T., & Warner, K. E. (eds.), Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press.Google Scholar
Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods 19: 459–81. doi: 10.1037/a0036434Google Scholar
Prince, M. (2008). Measurement validity in cross-cultural comparative research. Epidemiology and Psychiatric Science 17: 211–20. doi: 10.1017/S1121189X00001305Google Scholar
R Development Core Team (2007). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. www.R-project.orgGoogle Scholar
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage.Google Scholar
Raudenbush, S. W., Bryk, A. S., & Congdon, R. (2004). HLM 6 for Windows [computer software]. Skokie, IL: Scientific Software International, Inc.Google Scholar
Raudenbush, S. W., Spybrook, J., Congdon, R., Liu, X., & Martinez, A., Bloom, H., & Hill, C. (2011). Optimal Design Software Plus Empirical Evidence (Version 3.0) [software]. www.wtgrantfoundation.orgGoogle Scholar
Riecken, H. W., & Boruch, R. F. (1974). Social Experimentation: A Method for Planning and Evaluating Social Intervention. New York: Academic Press.Google Scholar
Rosseel, Y. (2012). lavaan: an R package for structural equation modeling. Journal of Statistical Software 48: 1–36. www.jstatsoft.org/v48/i02/Google Scholar
Rubin, D. B. (1976). Inference and missing data. Biometrika 63: 581–92. doi.org/10.1093/biomet/63.3.581Google Scholar
Rubin, D. B. (1978). Bayesian inference for causal effects: the role of randomization. Annals of Statistics 6: 34–58. doi.org/10.1214/aos/1176344064Google Scholar
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.Google Scholar
Rubin, D. B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association 91: 473–89. doi.org/10.1080/01621459.1996.10476908Google Scholar
Rubin, D. B. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association 100: 322–31. doi: 10.1198/016214504000001880Google Scholar
SAS Institute Inc. (2008). SAS/STAT® 9.2 User’s Guide. Cary, NC: SAS Institute Inc.Google Scholar
Sandler, I. N., Ingram, A., Wolchik, S. A., Winslow, E. B., & Tein, J.-Y. (in press). Long-term effects of parenting preventive interventions to promote resilience of children and adolescents. Child Development Perspectives.Google Scholar
Sandler, I. N., Wolchik, S. A., MacKinnon, D., Ayers, T. S., & Roosa, M. W. (1997). Developing linkages between theory and intervention in stress and coping processes. In Wolchik, S. A., & Sandler, I. N. (eds.), Handbook of Children’s Coping: Linking Theory and Intervention. New York: Plenum, pp. 3–40.Google Scholar
Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.Google Scholar
Schafer, J. L., & Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological Methods 7: 147–77. doi.org/10.1037/1082–989X.7.2.147Google Scholar
Schafer, J. L., & Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: a data analyst’s perspective. Multivariate Behavioral Research 33: 545–71. doi: 10.1207/s15327906mbr3304_5CrossRefGoogle ScholarPubMed
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin.Google Scholar
Shpitser, I. (2013). Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding. Cognitive Science 37: 1011–35. doi: 10.1111/cogs.12058Google Scholar
Snijders, T. A. B., & Bosker, R. J. (2012). An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed. Thousand Oaks, CA: Sage.Google Scholar
StataCorp (2013). Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.Google Scholar
Steiger, J.H. (1990). Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research 25: 173–80. doi.org/10.1207/s15327906mbr2502_4Google Scholar
Sullivan, C. M., Rumptz, M. H., Campbell, R., Eby, K. K., Davidson, W. S. II (1996). Retaining participants in longitudinal community research: a comprehensive protocol. Journal of Applied Behavioral Science 32: 262–76.Google Scholar
Taylor, A. B., MacKinnon, D., & Tein, J.-Y. (2008). Test of the three path mediated effect. Organization Research Methods 11: 241–69. doi: 10.1177/1094428107300344Google Scholar
Tein, J.-Y., Roosa, M.W., & Michaels, M. (1994). Agreement between parent and child reports on parental behaviors. Journal of Marriage and the Family 56: 341–55. doi.org/10.2307/353104Google Scholar
Tobler, N., & Stratton, H. (1997) Effectiveness of school-based drug prevention programs: a meta-analysis of the research. Journal of Primary Prevention, 18: 71–128. doi.org/10.1023/A:10246302059999Google Scholar
VanderWeele, T., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface 2: 457–68. doi.org/10.4310/SII.2009.v2.n4.a7Google Scholar
Widaman, K. F., & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Application in substance use domain. In Kendall, B. I., Windle, M. T., & West, S. G. (eds.), The Science of Prevention: Methodological Advances from Alcohol and Substance Abuse Research. Washington, DC: American Psychological Association, pp. 281–324.CrossRefGoogle Scholar
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: measuring the same construct across time. Child Development Perspectives 4: 10–18. doi.org/10.1111/j.1750–8606.2009.00110.xGoogle Scholar
Wilson, S. J., & Lipsey, M. J. (2007). Effectiveness of school-based intervention programs on aggressive behavior: update of a meta-analysis. American Journal of Preventive Medicine 33(Supplement 2): S130–S143. doi: 10.1016/j.amepre.2007.04.011Google Scholar
Wolchik, S. A., Sandler, I. N., Tein, J. Y., Mahrer, N., Millsap, R., Winslow, E. B., … Reed, A. B. (2013). Fifteen-year follow-up of a randomized trial of preventive intervention for divorced families: effects on mental health and substance use outcomes in young adulthood. Journal of Consulting and Clinical Psychology 81: 660–73. doi: 0091–0627/04/0400-0175/0CrossRefGoogle ScholarPubMed
Ye, C., Beyene, J., Browne, G., & Thabane, L. (2014). Estimating treatment effects in randomised controlled trials with non-compliance: a simulation study. BMJ Open 4: e005362. doi: 10.1136/bmjopen-2014-005362Google Scholar
Zhao, X., Lynch, J. G. Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: myths and truths about mediation analysis. Journal of Consumer Research 37: 197–206. doi: 10.1086/651257Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×