1. Software
2. Useful sites
3. General multilevel texts and estimation
4. Growth models/longitudinal data
5. Binary outcomes (HGLM models)
6. Cross-classified random effect models
7. Missing data and multiple imputation
8. Weighting
9. Latent variable analysis
10. Power
11. Mediation
12. Meta-Analysis
13. Multivariate (HMLM) models
14. Centering
15. Psychometric Applications
16. Assumptions and Diagnostics
17. FIRC models and automated imputation
18. Papers by Steve Raudenbush not listed above

• Bryk, A.S, Raudenbush, S.W., & Congdon, R. (1996). HLM 4 for Windows [Computer software]. Chicago, IL: Scientific Software International, Inc.
• Raudenbush, S.W., Bryk, A.S, & Congdon, R. (2000). HLM 5 for Windows [Computer software]. Lincolnwood, IL: Scientific Software International, Inc.
• Raudenbush, S.W., Bryk, A.S, & Congdon, R. (2004). HLM 6 for Windows [Computer software]. Lincolnwood, IL: Scientific Software International, Inc.
• Raudenbush, S.W., Bryk, A.S, Cheong, Y.F. & Congdon, R. (2011). HLM 7 for Windows [Computer software]. Lincolnwood, IL: Scientific Software International, Inc.
• Raudenbush, S.W., Bryk, A.S, Cheong, Y.F. & Congdon, R. (2019). HLM 8 for Windows [Computer software]. Skokie, IL: Scientific Software International, Inc. 

Useful sites: 

• Free copies of HLM papers from the Journal of Management can be found at http://www.findarticles.com/p/articles/

• Bock, R. (1975). Multivariate Statistical Methods in Behavioral Research. New York: McGrawHill.
• Breslow, N., & Clayton, D. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 925.
• Bryk, A., & Raudenbush, S. W. (1992). Hierarchical Linear Models for Social and Behavioral Research: Applications and Data Analysis Methods. Newbury Park, CA: Sage.
• Cheong, Y. F., Fotiu, R. P., & Raudenbush, S. W. (2001). Efficiency and robustness of alternative estimators for 2- and 3-level models: The case of NAEP. Journal of Educational and Behavioral Statistics, 26, 411-429.
• Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society, Series B(39), 18.
• Goldstein, H.I. (2003). Multilevel statistical models.(3rd Edition). London: Edward Arnold.
• Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.
• Jennrich, R., & Schluchter, M. (1986). Unbalanced repeated measures models with structured covariance matrices.Biometrics, 42, 805-820.
• Kreft, I., & de Leeuw, J. (1998). Introducing multilevel modeling. London: Sage.
• Longford, N. (1993). Random Coefficient Models. Oxford: Clarendon Press.
• McCullagh, P., & Nelder, J. (1989). Generalized Linear Models, 2nd Edition. London: Chapman and Hill.
• Pinheiro, J., & Bates, D. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model.Journal of Computational and Graphical Statistics, 4, 12-35.
• Raudenbush, S. (1993). A crossed random effects model for unbalanced data with applications in cross-sectional and longitudinal research. Journal of Educational Statistics, 18(4), 321-349.
• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, Second Edition. Newbury Park, CA: Sage.
• Raudenbush, S. W., & Sampson, R. (1999). Assessing direct and indirect associations in multilevel designs with latent variables. Sociological Methods and Research, 28(2), 123-153.
• Rodriguez, G., & Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the royal Statistical Society, A, 158, 73-89.
• Schall, R. (1991). Estimation in generalized linear models with random effects. Biometrika, 40, 719-727.
• Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.
• Yang, M. (1995). A simulation study for the assessment of the nonlinear hierarchical model estimation via approximate maximum likelihood. Unpublished apprenticeship paper, College of Education, Michigan State University.
• Yang, M.L. (1998). Increasing the efficiency in estimating multilevel Bernoulli models [Diss], East Lansing, MI: Michigan State University.

• Bryk, A.S., & Raudenbush, S.W. (1987). Application of hierarchical linear models to assessing change.Psychological Bulletin, 101, 147-158.
• Cudeck, R., Klebe, K.J. (2002). Multiphase mixed-effects models for repeated measures data. Psychological Methods, 7, 41-63.
• Elliot, D., Huizinga, D., & Menard, S. (1989). Multiple Problem Youth: Delinquency, Substance Use, and Mental Health Problems. New York: SpringerVerlag.
• Huttenlocher, J.E., Haight, W., Bryk, A.S., & Seltzer, M. (1991). Early vocabulary growth: Relations to language input and gender. Developmental Psychology, 22(2), 236-249.
• Miyazaki, Y., & Raudenbush, S.W. (2000). A test for linkage of multiple cohorts from an accelerated longitudinal design. Psychological Methods, 5, 44-63.
• Raudenbush, S. W. (2001). Toward a coherent framework for comparing trajectories of individual change. Collins, L., & Sayer, A. (Eds.), Best Methods for Studying Change (pp. 33-64).  Washington, DC: The American Psychological Association.
• Raudenbush, S. W., & Chan, W.S. (1993). Application of hierarchical linear models to study adolescent deviance in an overlapping cohort design. Journal of Clinical and Consulting Psychology, 61(6), 941-951.
• Raudenbush, S. W., Yang, M.l., & Yosef, M. (2000). Maximum likelihood for hierarchical models via high order, multivariate LaPlace approximation. Journal of Computational and Graphical Statistics, 9(1), 141-157.
• Singer, J.D., & Willett, J.B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford Press.
• Willet, J.B., Singer, J.D., & Martin, N.A. (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.
• Zeger, S., Liang, K.Y., & Albert, P. (1988). Models for longitudinal data: A likelihood approach. Biometrics, 44, 1049-1066.
• Zeger, S., & Liang, L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22.

• Fielding, A. (2003). Ordered category responses and random effects in multilevel and other complex structures. In S.P. Reise & N. Duan (Eds.), Multilevel modeling: Methodological advances, issues, and applications (pp. 181-208). Mahwah, NJ: Erlbaum.
• Goldstein, H. (1991). Non-linear multilevel models with an application to discrete response data. Biometrika, 78, 45-51.
• Hedeker, D., & Gibbons, R. (1994). A random effects ordinal regression model for multilevel analysis. Biometrics,pp. 993-944.
• Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erbaum. Chapter 6.
• Kang, S.J. (1992). A mixed linear model for unbalanced two-way crossed multilevel data with estimation via the EM algorithm. Unpublished doctoral dissertation, Michigan State University, East Lansing.
• Longford, N. (1993). Random coefficient models. London: Sage.
• Pinheiro, J., & Bates, D. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model.Journal of Computational and Graphical Statistics, 4, 12-35.
• Raudenbush, S. W., & Bhumirat, C. (1992). The distribution of resources for primary education and its consequences for educational achievement in Thailand. International Journal of Educational Research, pp. 143-164.
• Rodriquez, G., & Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A, 158, 73-89.
• Rowan, B., Raudenbush, S., & Cheong, Y. (1993). Teaching as a nonroutine task: Implications for the organizational design of schools. Educational Administration Quarterly, 29(4), 479-500.
• Rowan, R., Raudenbush, & Kang, S. (1991). Organizational design in high schools: A multilevel analysis. American Journal of Education, 99(2), 238-266.
• Snijders, T.A.B., & Bosker, R.J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.
• Stiratelli, R., Laird, N., & Ware, J. (1984). Random effects models for serial observations with binary response.Biometrics, 40, 961-971.
• Willet, J.B., Singer, J.D., & Martin, N.A. (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.
• Wong, G., & Mason, W. (1985). The hierarchical logistic regression model for multilevel analysis. Journal of the American Statistical Association, 80(391), 513-524.

• Garner, C., & Raudenbush, S. (1991). Neighborhood effects on educational attainment: A multi-level analysis of the influence of pupil ability, family, school, and neighborhood. Sociology of Education, 64(4), 251-262.
• Hill, P.W., & Goldstein, H. (1998). Multilevel modeling of educational data with cross-classification and missing identification of units. Journal of Educational and Behavioral Statistics, 23, 117-128.

Missing data and multiple imputation: 

• Little, R., & Rubin, D. (1987). Statistical analysis with missing data. New York: Wiley.
• Little, R., & Schenker, N. (1995). Missing data. In G. Arminger, C. C. Clogg & M. E. Sobel (Eds.), Handbook of Statistical Modeling for the Social and Behavioral Sciences (pp. 39-76). New York: Plenum Press.
• Rogers, A., & et. al. (1992). National Assessment of Educational Progress: 1990 Secondary-use Data Files User Guide. Princeton, New Jersey: Educational Testing Service.
• Rubin, D. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
• Schafer, J. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.

• Pfefferman, D., Skinner, C.J., Homes, D.J., Goldstein, H., and Rasbash, J. (1998). Weighting for unequal selection models in multilevel models. Journal of the Royal Statistical Society, Series B, 60, 1, 23-40.

• Raudenbush, S. W., & Sampson, R. (1999). Assessing direct and indirect associations in multilevel designs with latent variables. Sociological Methods and Research, 28(2), 123-153.

• Raudenbush, S.W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2, 173-185.
• Raudenbush, S.W., & Liu, X.. (2000). Statistical power and optimal design for multisite randomized trials.Psychological Methods, 5, 199-213.
• Snijders, T.A.B., & Bosker, R.J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237-259.

• Bauer, D. J., & Curran, P. J. (in press). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research.
• Curran, P. J., Bauer, D. J, & Willoughby, M. T. (in press). Testing and probing interactions in hierarchical linear growth models. To appear in C. S. Bergeman & S. M. Boker (Eds.), The Notre Dame Series on Quantitative Methodology, Volume 1: Methodological Issues in Aging Research. Mahwah, NJ: Lawrence Erlbaum Associates.
• 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.
• Kenny, D.A., Korchmaros, J.D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8, 115-128.
• Pituch, K. A., Whittaker, T. A., & Stapleton, L. M. (2005). A Comparison of Methods to Test for Mediation in Multisite Experiments. Multivariate Behavioral Research, 40, 1-23.
• Pituch, K. A., Stapleton, L. M., & Kang, J. Y. (2006). A comparison of single sample and bootstrap methods to assess mediation in cluster randomized trials.  Multivariate Behavioral Research, 41, 367-400.

• Hox, J.J., & de Leeuw, E.D. (2003). Multilevel models for meta-analysis. In S.P. Reise & N. Duan (Eds.),Multilevel modeling: Methodological advances, issues, and applications (pp. 90-111). Mahwah, NJ: Erlbaum.

• Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and data analysis methods (2nd Edition). Thousand Oaks, CA: Sage.
• Tate, R. L., & Pituch, K. A. (2007). Multivariate hierarchical linear modeling in randomized field experiments. Journal of Experimental Education, 75, 317-337.
• Thum, Y.M. (1997). Hierarchical linear models for multivariate outcomes. Journal of Educational and Behavioral Statistics, 22, 77-108.

• Kreft, I.G.G., de Leeuw, J., & Aiken, L. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research, 30, 1-22.

• Kreft, I.G.G. (1997). The interactive effect of alcohol prevention programs in high school classes: An illustration of item homogeneity scaling and multilevel analysis techniques. In K.J. Bryant, M. Windle, and S.G. West (eds.),Science of prevention: Methodological advances from alcohol and substance abuse research. Washington, D.C.: American Psychological Association.

• Ecob, R., & Der, G. (2003). An interative method for the detection of outliers in longitudinal growth data using multilevel models. In S.P. Reise & N. Duan (Eds.), Multilevel modeling: Methodological advances, issues, and applications (pp. 229-254). Mahwah, NJ: Erlbaum.
• Langford, I.H., & Lewis, T. (1998). Outliers in multilevel data. Journal of the Royal Statistical Society, Series A,161,121-160.
• Raudenbush, S.W., Bryk, A.S., Cheong, Y.F., & Congdon, R.T.,Jr (2000). HLM 5: Hierarchical linear and nonlinear modeling. (Statistical software manual). Skokie, IL: Scientific Software International.
• Seltzer, M., Novak, J., Choi, K., & Lim, N. (2002). Sensitivity analysis form hierarchical models employing t level-1 assumptions. Journal of Educational & Behavioral Statistics, 27, 181-222.

• Barnett, R. C., Brennan, R. T., Raudenbush, S. W., & Marshall, N. L. (1993). Gender and the relationship between marital role equality and psychological distress: A study of dual earner couples. Journal of Personality and Social Psychology, 64, 794-806.
• Raudenbush, S.W., & Willms, J.D. (l991). Pupils, Classrooms. and Schools: International Studies of Schooling from a Multilevel Perspective. New York: Academic Press.
• Raudenbush, S.W. (in press). Many small groups. To appear in, deLeeuw, Jan and Kreft, Ita (Eds.), Handbook of Quantitative Multilevel Analysis. Kluwer Press.
• Raudenbush, S. W. (1999). Hierarchical models. In S. Kotz (Ed.), Encyclopedia of Statistical Sciences, Update Volume 3 (pp. 318-323). New York: John Wiley & Sons, Inc.
• Raudenbush, S.W. (1993). Hierarchical linear models and experimental design. In Lynne K. Edwards (Ed.), Applied analysis of variance in behavioral science. New York: Marcel Dekker.
• Raudenbush, S.W., Hong, G., and Rowan, B. (in press). Studying the causal effects of instruction with application to primary-school mathematics. To appear in Ross, J. M., Bohrnstedt, G.W. and Hemphill, F.C. (editors),Instructional and Performance Consequences of High Poverty Schooling. National Council for Educational Statistics, Washington DC.
• Harrison, D. and Raudenbush, S.W. (in press). Linear regression and hierarchical linear models. To appear in Green, J., Camilli, G., and Elmore, P. (editors) Complementary Methods for Research in Education. Washington, DC: American Educational Research Association.
• Johnson, C and Raudenbush, S.W. (in press) A repeated measures, multilevel Rasch model with application to self-reported criminal behavior. To appear in Bergeman, C.S. & Boker, S.M. (eds.) Quantitative Methodology in Aging Research. Proceedings from the Notre Dame Series on Quantitative Methodology: Quantitative Methodology in Aging Research. Erlbaum Press.
• Sampson, R.J., Morenoff, J.D. and Raudenbush, S.W. (in press). Social anatomy of racial and ethnic disparities in violence. To appear in American Journal of Public Health.
• Sampson, R.J. & Raudenbush, S.W.(in press). The social structure of seeing disorder. To appear in Social Psychology Quarterly.
• Bingenheimer, J., Leventhal, T., Brooks-Gunn, J. and Raudenbush, S.W. (in press). Measurement equivalence for two dimensions of children’s home environments. To appear in Journal of Family Psychology.
• Raudenbush, S.W. (2004). What are value-added models estimating and what does this imply for statistical practice?  Journal of Educational and Behavioral Statistics, 29(1), 121-129.
• Bingenheimer, J. & Raudenbush, S.W. (2004). Statistical and substantive inferences in public health: Issues in the application of multilevel models. Annual Review of Public Health, 25, 53-77.
• Kang, S.J., Rowan, B., and Raudenbush, S.W. (2004). Estimating the effects of academic departments on organic design in high schools: A crossed-multilevel analysis. In Hoy, W.K. and Miskel, C. (eds.) Educational Administration, Policy, and Reform: Research and Measurement, (pp.123-152), Information Age Publishing.
• Ewing, R., Schmid, T.L., Killingsworth, R.E., Zlot, A.I. and Raudenbush, S.W. (2003). Relationship between urban sprawl and physical activity, obesity, and morbidity. The American Journal of Health Promotion, 18(1), 47-57.
• Raudenbush, S.W. , Johnson, C. and Sampson, R. J. (2003). A multivariate, multilevel Rasch model for self-reported criminal behavior. Sociological Methodology, Vol. 33(1), 169-211.
• Buka, S. L., Brennan, R.T., Rich-Edwards, J.W., Raudenbush, S.W. and Earls, F. (2003). Neighborhood support and the birth weight of urban infants. The American Journal of Epidemiology, 157(1), 1-8.
• Cohen, D.K., Raudenbush, S.W., & Ball, D.L. (2003). Resources, instruction, and research. Educational Evaluation and Policy Analysis, 25(2), 1-24.
• Raudenbush, S.W. (2003). The quantitative assessment of neighborhood social environment. In Kawachi, I and Berkman, L. (Eds.), Neighborhoods and Health (pp. 112-131). Oxford University Press.
• Raudenbush, S.W. (2003). [Comments on Measurement, Objectivity, and Trust by Theodore M. Porter.]Measurement, 1(4), 274-278.
• Cohen, D., K., Raudenbush, S. W., & Ball, D. L. (2002). Resources, Instruction, and Research. In F. Mosteller & R. Boruch (Eds.), Evidence matters: Randomized trials in education research, (pp. 80-119). Washington, DC: Brookings Institution Press.
• Raudenbush, S.W. (2002). Alternative Covariance Structures for Polynomial Models of Individual Growth and Change. In Moskowitz/Hershberger (Eds.), Modeling Intraindividual variability with repeated measures data: Methods and Applications (pp. 25-58). Mahway, New Jersey: Lawrence Erlbaum Associates.
• Raudenbush, S.W. (2002). Mixed modeling matures [Review of the books "Linear mixed models for longitudinal data" (and) "Mixed-effects models in S and S-Plus], by G. Verbeke, & G. Molenberghs (and) J. Pinheiro, & D. Bates (New York: Springer, 2000). Sociological Methods and Research, 31(1), 110-118.
• Raudenbush, S. W., & Kim, J.S. (2002). Statistical issues in analysis of international comparisons of educational achievement. In A. C. Porter & A. Gamoran (Eds.), Methodological Advances in CrossNational Surveys of Educational Achievement (pp. 267-294). Washington DC: National Academy Press.
• Cheong, Y.F., Fotiu, R.P, & Raudenbush, S.W. (2001). Efficiency and robustness of alternative estimators for 2- and 3- level models: The case of NAEP. To appear in Journal of Educational & Behavioral Statistics.
• 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(4), 387-401.
• Duncan, G.J. & Raudenbush, S.W. (2001). Neighborhoods and adolescent development: How can we determine the links? In Alan Booth and Nan Crouter (Eds.), Does it Take a Village? Community Effects on Children, Adolescents, and Families, [pp. 105-136]. State College, PA: Pennsylvania State University Press.
• Duncan, G.J. & Raudenbush, S.W. (2001). Getting context right in quantitative studies of child development. In Arland Thornton (Ed.), The Well-Being of Children and Families, [pp.356-383]. Ann Arbor, MI: The University of Michigan Press.
• Kerckhoff, A.C., Raudenbush, S.W. & Glennie, E. (2001). Education, cognitive skill, and labor force outcomes.Sociology of Education, 74(1), 1-24.
• Morenoff, J.D., Sampson, R.J. and Raudenbush, S.W. (2001). Neighborhood structure, social processes, and the spatial dynamics of urban violence. Criminology, 39(37), 517-560.
• Raudenbush, S.W. (2001). Comparing personal trajectories and drawing causal inferences from longitudinal data.Annual Review of Psychology, 52, 501-525.
• Sampson, R.J. & Raudenbush, S.W. (Feb. 2001). Disorder in urban neighborhoods – Does it lead to crime? National Institute of Justice, Research Brief.
• Raudenbush, S.W. (2001). Toward a coherent framework for comparing trajectories of individual change. Collins, L. and Sayer, A. (Eds.), New Methods for the Analysis of Change (pp.35-64). Washington D.C.: The American Psychological Association.
• Cheong, Y.F. & Raudenbush, S.W. (2000). Measurement and structural models for children’s problem behaviors.Psychological Methods, 5(4), 477-495.
• Kuo, M., Mohler, B., Raudenbush, S.W., & Earls, F.J. (2000). Assessing exposure too violence using multiple informants: Application of hierarchical linear model. The Journal of Child Psychology and Psychiatry, 41, 1049-1056.
• Miyazaki, Y & Raudenbush, S.W. (2000). A test for linkage of multiple cohorts from an accelerated longitudinal design. Psychological Methods, 5(1), 44-63.
• Raudenbush, S.W. (2000). Synthesizing Results for NAEP Trial State Assessment. In Grissmer, D.W. and Ross, Michael (Ed.), Analytic Issues in the Assessment of Student Achievement, Washington, DC: National center for Educational Statistics.
• Raudenbush, S.W. & Liu Xiaofeng. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5(3), 199-213.
• Raudenbush, S.W., Yang, Meng-Li & Yosef, M. (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics, 9(1), 141-157.
• Raudenbush, S.W. (1999). Hierarchical models.In S. Kotz, (Ed.), Encyclopedia of Statistical Sciences, Update Volume 3, (pp. 318-323). New York: John Wiley.
• Raudenbush, S.W., Fotiu, R.P. & Cheong, Y.F. (1999). Synthesizing results from trial state assesment. Journal of Educational and Behavioral Statistics, 24(4), 413-438.
• Raudenbush, S.W., & Sampson, R. (1999). Assessing direct and indirect effects in multilevel designs with latent variables. Sociological Methods & Research, 28(2),123-153.
• Raudenbush, S.W., & Sampson, R. (1999). Ecometrics: Toward a science of assessing ecological settings, with application to the systematic social observations of neighborhoods. Sociological Methodology, 29, 1-41.
• Sampson, R.J. & Raudenbush, S.W. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105(3), 603-651.
• Duncan, G.J., & Raudenbush, S.W. (1998). Assessing the effects of context in studies of child and youth development. Educational Psychologist, 34(1), 29-41.
• Kasim, R. & Raudenbush, S. (1998). Application of Gibbs sampling to nested variance components models with heterogenous with-in group variance. Journal of Educational and Behavioral Statistics, 23(2), 93-116.
• Raudenbush S.W. (1998, May). [Review of the book A solution to the ecological inference problem: Reconstructing individual behavior from aggregate data by Gary King.] American Journal of Sociology, 103(6), 1770-1772.
• Raudenbush, S.W., Fotiu, R.P., & Cheong, Y.F. (1998). Inequality of access to educational resources: A national report card for eighth grade math. Educational Evaluation and Policy Analysis, 20(4), 253-268.
• Raudenbush, S.W., & Kasim, R. (1998). Cognitive skill and economic inequality: Findings from the National Adult Literacy Survey. Harvard Educational Review, 68(1), 33-79.
• Sampson, R.J., Raudenbush, S.W., & Earls, F. (1998, April). Neighborhood collective Efficacy - does it help reduce violence? National Institute of Justice Research Preview, Washington, DC. Abstracted with permission from Sampson, R.J., Raudenbush, S.W. & Earls, F. “Neighborhoods & violent crime - A multilevel study of collective efficacy”, Science, 277, (1-7).
• Selner-O'Hagan, M.B., Kindlon, D.J., Buka, S.L., Raudenbush, S.W., & Earls, F.J. (1998). Assessing Exposure to Violence in Urban Youth. Journal of Child Psychology and Psychiatry and Allied Disciplines, 39(2), 215-224.
• Raudenbush, S.W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2(2), 173-185.
• Sampson, R.J., Raudenbush, S.W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918-924.
• Willms, J.D., & Raudenbush, S. (1997). Effective schools research: Methodological issues. In Saha, L.J. (Ed.),The International Encyclopaedia of the Sociology of Education (1934-1939). New York: Elsevier.
• Kalaian, H.A. & Raudenbush, S.W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1(3), 227-235.
• Kindlon, D.J., Wright, B.D., Raudenbush, S.W. & Earls, F. (1996). The measurement of children's exposure to violence: A Rasch analysis. International Journal of Methods in Psychiatric Research, 6, 187-194.
• Raudenbush, S.W., Cheong, Y.F., Fotiu, R.P. (1996). Section A, Social inequality, social segregation, and their relationship to reading literacy in 22 countries. In M. Binkley, K. Rust, and T. Williams (Eds.) The IEA Reading Literacy Study: The United States in International Perspective, (pp. 5-62), Washington: National Center for Educational Statistics.
• Raudenbush, S.W., Fotiu, R.P, Cheong, Y.F., & Ziazi, Z.M. (1996). Synthesizing results from the Trial State Assessment. American Statistical Association 1995 Proceedings of the Section on Survey Research Methods, 1, 257-262.