Organizations are multilevel systems. Most organizational phenomena are multilevel in nature, and their understanding involves variables (e.g., antecedents and consequences) that reside at different levels. The investigation of these phenomena requires appropriate analytical methods: multilevel modeling. These techniques are becoming increasingly popular among organizational psychology and organizational behavior (OPOB) researchers. In this article we review the literature that has evaluated the performance of multilevel modeling techniques to test multilevel direct and indirect effects and cross-level interactions. We also provide guidelines for OPOB researchers about the appropriate use of these techniques, and we suggest ways these techniques can contribute to theoretical advancement and research development in OPOB.

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Literature Cited

  1. Aguinis H, Culpepper SA. 2015. An expanded decision-making procedure for examining cross-level interaction effects with multilevel modeling. Organ. Res. Methods 18:155–76 [Google Scholar]
  2. Aguinis H, Gottfredson RK, Culpepper SA. 2013. Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. J. Manag 39:1490–528Comprehensive review of factors to consider when testing and interpreting cross-level interactions. [Google Scholar]
  3. Aguinis H, Werner S, Abbott JL, Angert C, Park JH, Kohlhausen D. 2010. Customer-centric science: reporting significant research results with rigor, relevance, and practical impact in mind. Organ. Res. Methods 13:515–39 [Google Scholar]
  4. Ansari A, Jedidi K, Dube L. 2002. Heterogeneous factor analysis models: a Bayesian approach. Psychometrika 67:49–78 [Google Scholar]
  5. Bassiri D. 1988. Large and small sample properties of maximum likelihood estimates for hierarchical linear models PhD Thesis Mich. State Univ. East Lansing:
  6. Bauer DJ. 2003. Estimating multilevel linear models as structural equation models. J. Educ. Behav. Stat. 28:135–67 [Google Scholar]
  7. Bauer DJ, Curran PJ. 2005. Probing interactions in fixed and multilevel regression: inferential and graphical techniques. Multivar. Behav. Res. 40:373–400 [Google Scholar]
  8. Bauer DJ, Preacher KJ, Gil KM. 2006. Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychol. Methods 11:142–63 [Google Scholar]
  9. Bell BA, Morgan GB, Schoeneberger JA, Kromrey JD, Ferron JM. 2014. How low can you go?. Methodology 1:86–92 [Google Scholar]
  10. Berkhof J, Snijders TA. 2001. Variance component testing in multilevel models. J. Educ. Behav. Stat. 26:133–52 [Google Scholar]
  11. Bliese PD. 2000. Within-group agreement, non-independence, and reliability: implications for data aggregation and analysis. Multilevel Theory, Research, and Methods in Organizations KJ Klein, SWJ Kozlowski 349–81 San Francisco: Jossey-Bass [Google Scholar]
  12. Bliese PD, Hanges PJ. 2004. Being both too liberal and too conservative: the perils of treating grouped data as though they were independent. Organ. Res. Methods 7:400–17 [Google Scholar]
  13. Bosker RJ, Snijders TAB, Guldemond H. 2003. PINT (Power IN Two-level designs): Estimating Standard Errors of Regression Coefficients in Hierarchical Linear Models for Power Calculations: User's Manual (Version 2.1) Groningen, Neth.: Neth. Org. Sci. Res. https://www.stats.ox.ac.uk/∼snijders/Pint21_UsersManual.pdf
  14. Browne WJ, Lahi MG, Parker RMA. 2009. A Guide to Sample Size Calculations for Random Effect Models via Simulation and the MLPowSim Software Package Bristol, UK: Univ. Bristol
  15. Bryk AS, Raudenbush SW. 1992. Hierarchical Linear Models: Applications and Data Analysis Methods Thousand Oaks, CA: Sage
  16. Burstein L, Linn RL, Capell FJ. 1978. Analyzing multilevel data in the presence of heterogeneous within-class regressions. J. Educ. Stat. 3:347–83 [Google Scholar]
  17. Chen G. 2005. Newcomer adaptation in teams: multilevel antecedents and outcomes. Acad. Manag. J. 48:101–16 [Google Scholar]
  18. Chen G, Bliese PD, Mathieu JE. 2005. Conceptual framework and statistical procedures for delineating and testing multilevel theories of homology. Organ. Res. Methods 8:375–409 [Google Scholar]
  19. Chen G, Kirkman BL, Kanfer R, Allen D, Rosen B. 2007. A multilevel study of leadership, empowerment, and performance in teams. J. Appl. Psychol. 92:331–46 [Google Scholar]
  20. Cheung MWL, Au K. 2005. Applications of multilevel structural equation modeling to cross-cultural research. Struct. Equ. Model. 12:598–619 [Google Scholar]
  21. Cohen JE. 1988. Statistical Power Analysis for the Behavioral Sciences Hillsdale, NJ: Lawrence Erlbaum Assoc., Inc.
  22. Croon MA, van Veldhoven JPM. 2007. Predicting group-level outcome variables from variables measured at the individual level: a latent variable multilevel model. Psychol. Methods 12:45–57 [Google Scholar]
  23. Curran PJ. 2003. Have multilevel models been structural equation models all along. Multivar. Behav. Res. 38:529–69 [Google Scholar]
  24. Dalal DK, Zickar MJ. 2012. Some common myths about centering predictor variables in moderated multiple regression and polynomial regression. Organ. Res. Methods 15:339–62 [Google Scholar]
  25. de Leeuw J, Kreft I. 1986. Random coefficient models for multilevel analysis. J. Educ. Stat. 11:57–85 [Google Scholar]
  26. Enders C. 2013. Centering predictors and contextual effects. The SAGE Handbook of Multilevel Modeling MA Scott, JS Simonoff, BD Marx 89–108 London: Sage [Google Scholar]
  27. Enders CK, Tofighi D. 2007. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychol. Methods 12:121–38Detailed overview and practical recommendations about centering options, with and without contextual effects. [Google Scholar]
  28. Finch WH, French BF. 2011. Estimation of MIMIC model parameters with multilevel data. Struct. Equ. Model. 18:229–52 [Google Scholar]
  29. Gavin MB, Hofmann DA. 2002. Using hierarchical linear modeling to investigate the moderating influence of leadership climate. Leadersh. Quart. 13:15–33 [Google Scholar]
  30. Goldstein H. 1986. Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika 73:43–56 [Google Scholar]
  31. Goldstein H, McDonald RP. 1988. A general model for the analysis of multilevel data. Psychometrika 53:455–67 [Google Scholar]
  32. González-Romá V, Hernández A. 2014. Climate uniformity: its influence on team communication quality, task conflict, and team performance. J. Appl. Psychol. 99:1042–58 [Google Scholar]
  33. Griffin MA. 1997. Interaction between individuals and situations: using HLM procedures to estimate reciprocal relationships. J. Manag. 23:759–73 [Google Scholar]
  34. Guenole N. 2016. The importance of isomorphism for conclusions about homology: a Bayesian multilevel structural equation modeling approach with ordinal indicators. Front. Psychol. 7:2891–17 [Google Scholar]
  35. Hackman JR. 2003. Learning more by crossing levels: evidence from airplanes, hospitals, and orchestras. J. Organ. Behav. 24:905–22 [Google Scholar]
  36. Heck RH, Thomas SL, Tabata LN. 2013. Multilevel and Longitudinal Modeling with IBM SPSS New York: Routledge
  37. Heck RH, Thomas SL. 2015. An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus New York: RoutledgeAn easy-to-follow introduction to CMLM and MSEM.
  38. Hedges LV. 2007. Effect sizes in cluster randomized designs. J. Educ. Behav. Stat. 32:341–70 [Google Scholar]
  39. Hedges LV. 2011. Effect sizes in three-level cluster-randomized experiments. J. Educ. Behav. Stat. 36:346–80 [Google Scholar]
  40. Hitt MA, Beamish PW, Jackson SE, Mathieu JE. 2007. Building theoretical and empirical bridges across levels: multilevel research in management. Acad. Manag. Rev. 50:1385–99 [Google Scholar]
  41. Hofmann DA. 1997. An overview of the logic and rationale of hierarchical linear models. J. Manag 23:723–44Introductory tutorial to CMLM, with an overview of typical series of models to be investigated. [Google Scholar]
  42. Hofmann DA, Gavin MB. 1998. Centering decisions in hierarchical linear models: implications for research in organizations. J. Manag. 24:623–41 [Google Scholar]
  43. Hofmann DA, Morgeson FP, Gerras SJ. 2003. Climate as a moderator of the relationship between leader-member exchange and content specific citizenship: safety climate as an exemplar. J. Appl. Psychol. 88:170–78 [Google Scholar]
  44. House R, Rousseau DM, Thomas-Hunt M. 1995. The meso paradigm: a framework for the integration of micro and macro organizational behavior. Res. Organ. Behav. 17:71–114 [Google Scholar]
  45. House RJ, Hanges PJ, Javidan M, Dorfman PW, Gupta V. 2004. Culture, Leadership, and Organizations: The GLOBE Study of 62 Societies Thousand Oaks, CA: Sage
  46. Hox JJ. 2002. Multilevel Analysis Mahwah, NJ: Lawrence Earlbaum
  47. Hox JJ. 2010. Multilevel Analysis: Techniques and Applications New York: Routledge, 2nd ed..
  48. Hox JJ, Maas CJ. 2001. The accuracy of multilevel structural equation modeling with pseudobalanced groups and small samples. Struct. Equ. Model. 8:157–74 [Google Scholar]
  49. Hox JJ, Moerbeek M, Kluytmans A, van de Schoot R. 2014. Analyzing indirect effects in cluster randomized trials. The effect of estimation method, number of groups and group sizes on accuracy and power. Front. Psychol. 5:781–7 [Google Scholar]
  50. Hox JJCM, van de Schoot R, Matthijsse S. 2012. How few countries will do? Comparative survey analysis from a Bayesian perspective. Surv. Res. Methods 6:87–93 [Google Scholar]
  51. Huhtala M, Tolvanen A, Mauno S, Feldt T. 2015. The associations between ethical organizational culture, burnout, and engagement: a multilevel study. J. Bus. Psychol. 30:399–414 [Google Scholar]
  52. Julian MW. 2001. The consequences of ignoring multilevel data structures in nonhierarchical covariance modeling. Struct. Equ. Model. 8:325–52 [Google Scholar]
  53. Kenny DA, Korchmaros JD, Bolger N. 2003. Lower level mediation in multilevel models. Psychol. Methods 8:115–28 [Google Scholar]
  54. Kim KS. 1990. Multilevel data analysis: a comparison of analytical alternatives PhD Thesis Univ. Calif. Los Angeles:
  55. Klein KJ, Dansereau F, Hall RJ. 1994. Levels issues in theory development, data-collection, and analysis. Acad. Manag. Rev. 19:195–229 [Google Scholar]
  56. Konstantopoulos S. 2008. The power of the test for treatment effects in three-level cluster randomized designs. J. Res. Educ. Eff. 1:66–88 [Google Scholar]
  57. Konstantopoulos S. 2009. Incorporating cost in power analysis for three-level cluster- randomized designs. Eval. Rev. 33:335–57 [Google Scholar]
  58. Kozlowski SWJ, Klein KJ. 2000. A multilevel approach to theory and research in organizations. Contextual, temporal, and emergent processes. Multilevel Theory, Research, and Methods in Organizations KJ Klein, SWJ Kozlowski 3–90 San Francisco: Jossey-Bass [Google Scholar]
  59. Kreft I, de Leeuw J. 1998. Introducing Multilevel Modeling London: Sage
  60. Kreft IGG, de Leeuw J, Aiken LS. 1995. The effect of different forms of centering in hierarchical linear models. Multivar. Behav. Res. 30:1–21 [Google Scholar]
  61. Krull JL, MacKinnon DP. 1999. Multilevel mediation modeling in group-based intervention studies. Eval. Rev. 23:418–44 [Google Scholar]
  62. Krull JL, MacKinnon DP. 2001. Multilevel modeling of individual and group level mediated effects. Multivar. Behav. Res. 36:249–77 [Google Scholar]
  63. Lance CE, Vandenberg RJ. 2015. More Statistical and Methodological Myths and Urban Legends New York: Routledge
  64. LaHuis DM, Ferguson MW. 2009. The accuracy of significance tests for slope variance components in multilevel random coefficient models. Organ. Res. Methods 12:418–35 [Google Scholar]
  65. LaHuis DM, Hartman MJ, Hakoyama S, Clark PC. 2014. Explained variance measures for multilevel models. Organ. Res. Methods 17:433–51 [Google Scholar]
  66. LeBreton JM, Senter JL. 2008. Answers to twenty questions about interrater reliability and interrater agreement. Organ. Res. Methods 11:815–52 [Google Scholar]
  67. Lee S, Dalal RS. 2016. Climate as situational strength: Safety climate strength as a cross-level moderator of the relationship between conscientiousness and safety behaviour. Eur. J. Work Organ. Psy. 25:120–32 [Google Scholar]
  68. Li X, Beretvas SN. 2013. Sample size limits for estimating upper level mediation models using multilevel SEM. Struct. Equ. Model. 20:241–64 [Google Scholar]
  69. LoPilato AC, Vandenberg RJ. 2015. The not-so-direct cross-level direct effect. See Lance & Vandenberg 2015 292–310
  70. Lüdtke O, Marsh HW, Robitzsch A, Trautwein U. 2011. A 2×2 taxonomy of multilevel latent contextual models: accuracy–bias trade-offs in full and partial error correction models. Psychol. Methods 16:444–67Shows how measurement and sampling error affect estimates of contextual effects and provides syntax. [Google Scholar]
  71. Lüdtke O, Marsh HW, Robitzsch A, Trautwein U, Asparouhov T, Muthén B. 2008. The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies. Psychol. Methods 13:203–29 [Google Scholar]
  72. Lüdtke O, Robitzsch A, Trautwein U, Kunter M. 2009. Assessing the impact of learning environments: how to use student ratings of classroom or school characteristics in multilevel modeling. Contemp. Educ. Psychol. 34:120–31 [Google Scholar]
  73. Maas CJ, Hox JJ. 2000. Robustness of multilevel parameter estimates against small sample sizes Presented at Int. Conf. Log. Methodol., 5th, Cologne, Germany
  74. Maas CJ, Hox JJ. 2004a. The influence of violations of assumptions on multilevel parameter estimates and their standard errors. Comput. Stat. Data An. 46:427–40 [Google Scholar]
  75. Maas CJ, Hox JJ. 2004b. Robustness issues in multilevel regression analysis. Stat. Neerl. 58:127–37 [Google Scholar]
  76. Maas CJ, Hox JJ. 2005. Sufficient sample sizes for multilevel modeling. Methodology 1:86–92 [Google Scholar]
  77. MacKinnon DP, Fairchild AJ, Fritz MS. 2007. Mediation analysis. Annu. Rev. Psychol. 58:593–614 [Google Scholar]
  78. Marsh HW, Lüdtke O, Robitzsch A, Trautwein U, Asparouhov T. et al. 2009. Doubly-latent models of school contextual effects: integrating multilevel and structural equation approaches to control measurement and sampling error. Multivar. Behav. Res. 44:764–802 [Google Scholar]
  79. Mathieu JE, Aguinis H, Culpepper SA, Chen G. 2012. Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. J. Appl. Psychol. 97:951–66 [Google Scholar]
  80. Mathieu JE, Chen G. 2011. The etiology of the multilevel paradigm in management research. J. Manag. 37:610–41 [Google Scholar]
  81. Mathieu JE, Taylor SR. 2007. A framework for testing meso‐mediational relationships in Organizational Behavior. J. Organ. Behav. 28:141–72 [Google Scholar]
  82. McDonald RP, Goldstein H. 1989. Balanced versus unbalanced designs for linear structural relations in two-level data. Br. J. Math. Stat. Psychol. 42:215–32 [Google Scholar]
  83. Mehta PD, Neale MC. 2005. People are variables too: multilevel structural equations modeling. Psychol. Methods 10:259–84 [Google Scholar]
  84. Meuleman B, Billiet J. 2009. A Monte Carlo sample size study: How many countries are needed for accurate multilevel SEM. Surv. Res. Methods 3:45–58 [Google Scholar]
  85. Mok M. 1995. Sample size requirements for 2-level designs in educational research. Multilevel Modelling Newsletter 7:2 June. http://www.bristol.ac.uk/media-library/sites/cmm/migrated/documents/new7-2.pdf [Google Scholar]
  86. Moerbeek M. 2004. The consequence of ignoring a level of nesting in multilevel analysis. Multivar. Behav. Res. 39:129–49 [Google Scholar]
  87. Muthén B. 1989. Latent variable modeling in heterogeneous populations. Psychometrika 54:557–85 [Google Scholar]
  88. Muthén B, Asparouhov T. 2008. Growth mixture modeling: analysis with non-Gaussian random effects. Longitudinal Data Analysis G Fitzmaurice, M Davidian, G Verbeke, G Molenberghs 143–65 Boca Raton, FL: Chapman & Hall/CRC [Google Scholar]
  89. Muthén B, Satorra A. 1995. Complex sample data in structural equation modeling. Sociol. Methodol. 25:267–316 [Google Scholar]
  90. Muthén LK, Muthén B. 2015. Mplus User's Guide Los Angeles, CA: Muthén & Muthén
  91. Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4:133–42 [Google Scholar]
  92. Naumann SE, Bennett N. 2000. A case for procedural justice climate: Development and test of a multilevel model. Acad. Manag. J 43:881–89 [ Erratum] [Google Scholar]
  93. Neal A, Griffin MA. 2006. A study of the lagged relationships among safety climate, safety motivation, safety behavior, and accidents at the individual and group levels. J. Appl. Psychol. 1:946–53 [Google Scholar]
  94. Paccagnella O. 2006. Centering or not centering in multilevel models? The role of the group mean and the assessment of group effects. Eval. Rev. 30:66–85 [Google Scholar]
  95. Peiró JM. 2008. Stress and coping at work: new research trends and their implications for practice. The Individual in the Changing Working Life K Näswall, J Hellgren, M Sverke 284–310 New York: Cambridge Univ. Press [Google Scholar]
  96. Pituch KA, Stapleton LM. 2008. The performance of methods to test upper-level mediation in the presence of nonnormal data. Multivar. Behav. Res. 43:237–67 [Google Scholar]
  97. Pituch KA, Stapleton LM. 2012. Distinguishing between cross-and cluster-level mediation processes in the cluster randomized trial. Sociol. Method. Res. 41:630–70 [Google Scholar]
  98. Preacher KJ. 2011. Multilevel SEM strategies for evaluating mediation in three-level data. Multivar. Behav. Res. 43:691–731 [Google Scholar]
  99. Preacher KJ, Curran PJ, Bauer DJ. 2006. Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. J. Educ. Behav. Stat. 31:437–48 [Google Scholar]
  100. Preacher KJ, Zhang Z, Zyphur MJ. 2011. Alternative methods for assessing mediation in multilevel data: the advantages of multilevel SEM. Struct. Equ. Model. 18:161–82 [Google Scholar]
  101. Preacher KJ, Zhang Z, Zyphur MJ. 2016. Multilevel structural equation models for assessing moderation within and across levels of analysis. Psychol. Methods 21:189–205 [Google Scholar]
  102. Preacher KJ, Zyphur MJ, Zhang Z. 2010. A general multilevel SEM framework for assessing multilevel mediation. Psychol. Methods 15:209–33Provides a framework and syntax to test for multilevel mediation within MSEM. [Google Scholar]
  103. Purvanova RK, Bono JE, Dzieweczynski J. 2006. Transformational leadership, job characteristics, and organizational citizenship performance. Hum. Perform. 19:1–22 [Google Scholar]
  104. Quinn RE, Spreitzer GM. 1991. The psychometrics of the competing values culture instrument and an analysis of the impact of organizational culture on quality of life. Res. Org. Change Dev. 5:115–42 [Google Scholar]
  105. Rabe-Hesketh S, Skrondal A, Pickles A. 2004. Generalized multilevel structural equation modeling. Psychometrika 69:167–90 [Google Scholar]
  106. Raudenbush SW. 1997. Statistical analysis and optimal design for cluster randomized trials. Psychol. Methods 2:173–85 [Google Scholar]
  107. Rousseau DM. 1985. Issues of level in organizational research: multi-level and cross-level perspectives. Research in Organizational Behavior 7 LL Cummings, B Staw 1–38 Greenwich, CT: JAI [Google Scholar]
  108. Ryu E. 2011. Effects of skewness and kurtosis on normal-theory based maximum likelihood test statistic in multilevel structural equation modeling. Behav. Res. Methods 43:1066–74 [Google Scholar]
  109. Ryu E. 2014. Model fit evaluation in multilevel structural equation models. Front. Psychol 5:811–9Reviews level-specific tests and indices to assess fit in MSEM. [Google Scholar]
  110. Ryu E, West SG. 2009. Level-specific evaluation of model fit in multilevel structural equation modeling. Struct. Equ. Model. 16:583–601 [Google Scholar]
  111. Scheipl F, Greven S, Küchenhoff H. 2008. Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Comput. Stat. Data An. 52:3283–99 [Google Scholar]
  112. Scherbaum CA, Ferreter JM. 2009. Estimating statistical power and required sample sizes for organizational research using multilevel modeling. Organ. Res. Methods 12:347–67 [Google Scholar]
  113. Schermelleh-Engel K, Kerwer M, Klein AG. 2014. Evaluation of model fit in nonlinear multilevel structural equation modeling. Front. Psychol. 5:1811–11 [Google Scholar]
  114. Schneider B. 1987. The people make the place. Per. Psychol. 14:437–53 [Google Scholar]
  115. Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ. 2012. A practical guide to calculating Cohen's f2, a measure of local effect size, from PROC MIXED. Front. Psychol. 3:1111–6 [Google Scholar]
  116. Snijders TA., Bosker RJ. 1993. Standard errors and sample sizes for two-level research. J. Educ. Behav. Stat. 18:237–59 [Google Scholar]
  117. Snijders TA, Bosker RJ. 1994. Modeled variance in two-level models. Socio. Meth. Res. 22:342–63 [Google Scholar]
  118. Snijders TA, Bosker RR. 1999. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling London: Sage
  119. Sobel ME. 1982. Asymptotic confidence intervals for indirect effects in structural equation models. Sociol. Methodol. 13:290–312 [Google Scholar]
  120. Stegmueller D. 2013. How many countries for multilevel modeling? A comparison of frequentist and Bayesian approaches. Am. J. Polit. Sci. 57:748–61 [Google Scholar]
  121. Teerenstra S, Moerbeek M, van Achterberg T, Pelzer BJ, Borm JF. 2008. Sample size calculations for 3-level cluster randomized trials. Clin. Trials 5:486–95 [Google Scholar]
  122. Tofighi D, Thoemmes F. 2014. Single-level and multilevel mediation analysis. J. Early Adolesc. 34:93–119 [Google Scholar]
  123. Tonidandel S, Williams EB, LeBreton JM. 2015. Size matters... Just not in the way that you think. See Lance & Vandenberg 2015 162–83
  124. van der Leeden R, Busing FM, Meijer E. 1997. Applications of bootstrap methods for two-level models Presented at Int. Multilevel Conf., 1st, Amsterdam, The Netherlands
  125. Yuan KH, Bentler PM. 2007. Multilevel covariance structure analysis by fitting multiple single‐level models. Sociol. Methodol. 37:53–82 [Google Scholar]
  126. Yuan Y, MacKinnon DP. 2009. Bayesian mediation analysis. Psychol. Methods 14:301–22 [Google Scholar]
  127. Yuan Y, MacKinnon DP. 2014. Robust mediation analysis based on median regression. Psychol. Methods 19:1–20 [Google Scholar]
  128. Zhang Z, Zyphur MJ, Preacher KJ. 2009. Testing multilevel mediation using hierarchical linear models problems and solutions. Organ. Res. Methods 12:695–719Describes different mediation models, their potential confounding of between and within effects, and solutions. [Google Scholar]

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