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***To suggest additional articles for this list, please go to the project [main page][1] and insert the citation as a comment (see the blue speech-bubble tab in the upper right corner). We will periodically add suggested papers to the main list, below.*** **I. RESEARCH METHODS** **Experimental Design:** Collins, L.M., Dziak, J.J., & Li, R. (2009). Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psychological methods, 14(3), 202. [Fulltext][3] Collins, L. M., Baker, T. B., Mermelstein, R. J., Piper, M. E., Jorenby, D. E., Smith, S. S., ... & Fiore, M. C. (2011). The multiphase optimization strategy for engineering effective tobacco use interventions. Annals of behavioral medicine,41(2), 208-226. [Fulltext][4] Kover, S.T., & Atwood, A.K. (2013). Establishing equivalence: Methodological progress in group-matching design and analysis. American Journal of Intellectual and Developmental Disabilities, 118, 3-15. [Link][5] Mervis, C.B., & Klein-Tasman, B.P. (2004). Methodological issues in group-matching designs: α levels for control variable comparisons and measurement characteristics of control and target variables. Journal of Autism and Developmental Disorders, 34, 7-17. [Fulltext][6] **General research methods:** Martin, J. (1980). A garbage can model of the research process. In J. E. McGrath, J. Martin, & R. A. Kulka, Judgment calls in research (pp. 17–39). Beverly Hills, CA. [Link][81] **Multilevel and/or Longitudinal Design:** Duncan, S. C., Duncan, T. E., & Hops, H. (1996). Analysis of longitudinal data within accelerated longitudinal designs. Psychological Methods, 1(3), 236. [Link][7] Nye, B., Konstantopoulos, S., & Hedges, L. V. (2004). How large are teacher effects?. Educational evaluation and policy analysis, 26(3), 237-257. [Fulltext][8] **Philosophy of Science:** Mayo, D.G., & Spanos, A. (2006). Severe testing as a basic concept in a Neyman-Pearson philosophy of induction. British Journal of the Philosophy of Science, 57, 323-357. [Fulltext][9] **Power and Sample Size:** Bakker, M., van Dijk, A., & Wicherts, J.M. (2012). The rules of the game called psychological science. Perspectives on Psychological Science, 7, 543-554. [Fulltext][10] Button, K.S., Ioannidis, J.P.A., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S.J., & Munafo, M.R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365-376. [Fulltext][11] Killip, S., Mahfoud, Z., & Pearce, K. (2004). What is an intracluster correlation coefficient? Crucial concepts for primary care researchers. The Annals of Family Medicine, 2(3), 204-208. [Fulltext][12] Maas, C. J., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling.Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1(3), 86. [Fulltext][13] 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(4), 599-620. [Fulltext][14] Spybrook, J., Raudenbush, S. W., Liu, X. F., Congdon, R., & Martínez, A. (2006). Optimal design for longitudinal and multilevel research: Documentation for the “Optimal Design” software. Survey Research Center of the Institute of Social Research at University of Michigan. [Fulltext][15] **Qualitative Methods:** Todd, Z., Nerlich, B., McKeown, S., & Clarke, D. D. (2004). Mixing Methods in Psychology: The Integration of Qualitative and Quantitative Methods in Theory and Practice. Psychology Press. [Link][84] **Replicable Science and Questionable Research Practices:** Brown, S. D., Furrow, D., Hill, D. F., Gable, J. C., Porter, L. P., & Jacobs, W. J. (2014). A Duty to Describe Better the Devil You Know Than the Devil You Don’t. Perspectives on Psychological Science, 9, 626-640. [Link][90] Ellemers, N. (2013). Connecting the dots: Mobilizing theory to reveal the big picture in social psychology (and why we should do this). European Journal of Social Psychology, 43, 1-8. [Link][16] Fuchs, H.M., Mirjam, J., & Fiedler, S. (2012). Psychologists are open to change, yet wary of rules. Perspectives on Psychological Science, 7, 639-642. [Fulltext][17] John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23, 524-532. [Fulltext][65] **Self- and Informant Report Methods:** Bartoshuk, L.M., Fast, K., & Snyder, D.J. (2005). Differences in our sensory worlds: Invalid comparisons with labeled scales. Current Directions in Psychological Science, 14, 122-125. [Link][2] Vazire, S. (2006). Informant reports: A cheap, fast, and easy method for personality assessment. Journal of Research in Personality, 40, 472-481. [Fulltext][66] **Validity:** Brewer, M. B. (2000). Research design and issues of validity. Handbook of research methods in social and personality psychology, 3-16. [Fulltext][82] Loevinger, J. (1957). Objective tests as instruments of psychological theory: Monograph Supplement 9. Psychological Reports, 3, 635-694. [Link][18] **II. DATA ANALYSIS** **Uses and Misuses of Statistics:** Scarr, S. (1997). Rules of evidence: A larger context for the statistical debate. Psychological Science, 8, 16-17. [Fulltext][19] Savalei, V., & Dunn, E. (2015). Is the call to abandon p-values the red herring of the replicability crisis?. Frontiers in Psychology, 6:245. [Fulltext][20] **Applied Problems:** Cramer, A. O., Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S. H., ... & Borsboom, D. (2012). Dimensions of normal personality as networks in search of equilibrium: You can't like parties if you don't like people. European Journal of Personality, 26(4), 414-431. [Fulltext][21] Hyde, J. S. (1994). Can meta-analysis make feminist transformations in psychology?. Psychology of Women Quarterly, 18, 451-462. [Link][70] van de Leemput, I. A., Wichers, M., Cramer, A. O., Borsboom, D., Tuerlinckx, F., Kuppens, P., ... & Scheffer, M. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences, 111(1), 87-92. [Fulltext][23] Vazire, S., & Gosling, S. D. (2004). e-Perceptions: personality impressions based on personal websites. Journal of personality and social psychology, 87(1), 123. [Fulltext][24] **Biological Psychology (neuro, geno):** Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V., & van der Sluis, S. (2014). A solution to dependency: using multilevel analysis to accommodate nested data. Nature neuroscience, 17(4), 491-496. [Link][25] Allen, E. A., Erhardt, E. B., & Calhoun, V. D. (2012). Data visualization in the neurosciences: overcoming the curse of dimensionality. Neuron, 74(4), 603-608. [Fulltext][26] Bassett, D. S., & Bullmore, E. D. (2006). Small-world brain networks. The neuroscientist, 12(6), 512-523. [Fulltext][27] Erez, Y., Tischler, H., Moran, A., & Bar-Gad, I. (2010). Generalized framework for stimulus artifact removal. Journal of neuroscience methods, 191(1), 45-59. [Fulltext][28] Franić, S., Dolan, C. V., Borsboom, D., Hudziak, J. J., van Beijsterveldt, C. E., & Boomsma, D. I. (2013). Can genetics help psychometrics? Improving dimensionality assessment through genetic factor modeling. Psychological methods, 18(3), 406. [Fulltext][29] Logan, J. A., Petrill, S. A., Hart, S. A., Schatschneider, C., Thompson, L. A., Deater-Deckard, K., ... & Bartlett, C. (2012). Heritability across the distribution: An application of quantile regression. Behavior genetics, 42(2), 256-267. [Fulltext][30] Medland, S. E., Neale, M. C., Eaves, L. J., & Neale, B. M. (2009). A note on the parameterization of Purcell’s G× E model for ordinal and binary data. Behavior genetics, 39(2), 220-229. [Fulltext][31] Mills, K. L., & Tamnes, C. K. (2014). Methods and considerations for longitudinal structural brain imaging analysis across development. Developmental cognitive neuroscience, 9, 172-190. [Link][32] Mumford, J. A. (2012). A power calculation guide for fMRI studies. Social cognitive and affective neuroscience, 7(6), 738-742. [Fulltext][74] Mumford, J. A., & Poldrack, R. A. (2007). Modeling group fMRI data. Social cognitive and affective neuroscience, 2(3), 251-257. [Fulltext][33] Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N., & Fischl, B. (2013). Spurious group differences due to head motion in a diffusion MRI study. NeuroImage, 88, 79–90. [Fulltext][34] **Confidence Intervals:** Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods, 10, 389-396. [Fulltext][35] Fidler, F., & Loftus, G.R. (2009). Why figures with error bars should replace p values. Journal of Psychology, 217, 27-37. [Fulltext][36] **Dyadic data analysis:** Kashy, D. A., & Kenny, D. A. (2000). The analysis of data from dyads and groups. In H.T. Reis & C.M. Judd (Eds.), Handbook of research methods in social psychology (pp. 451-477). New York: Cambridge University Press. [Link][83] **Effect Size:** Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in medicine, 19(22), 3127-3131. [Fulltext][37] Hill, C.J., Bloom, H.S., Black, A.R., & Lipsey, M.W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2(3), 172-177. [Fulltext][38] **Latent Change Score Modeling:** Quinn, J. M., Wagner, R. K., Petscher, Y., & Lopez, D. (2014). Developmental Relations Between Vocabulary Knowledge and Reading Comprehension: A Latent Change Score Modeling Study. Child development. **Latent Class Analysis:** Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural equation modeling, 14(4), 535-569. [Fulltext][40] **Logistic Models:** Azen, R., & Traxel, N. (2009). Using dominance analysis to determine predictor importance in logistic regression. Journal of Educational and Behavioral Statistics, 34(3), 319-347. [Fulltext][41] Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in medicine, 19(22), 3127-3131. [Fulltext][42] O'Connell, A. A. (2006). Logistic regression models for ordinal response variables (Vol. 146). Thousand Oaks, California:: Sage Publications. [Link][43] O'Connell, A. A., & McCoach, D. B. (Eds.). (2008). Multilevel modeling of educational data. IAP. [Link][44] Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3-14. [Fulltext][45] Yelland, L. N., Salter, A. B., Ryan, P., & Laurence, C. O. (2011). Adjusted intraclass correlation coefficients for binary data: methods and estimates from a cluster-randomized trial in primary care. Clinical Trials, 8(1), 48-58. [Link][46] **Longitudinal Analysis:** Collins, L. M., & Sayer, A. G. (2001). New methods for the analysis of change. American Psychological Association. [Link][67] Hamaker, E. L., Nesselroade, J. R., & Molenaar, P. C. (2007). The integrated trait–state model. Journal of Research in Personality, 41(2), 295-315. [Fulltext][47] Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata. STATA press. [Link][48] **Meta-Analysis** Chan, M.E., & Arvey, R.D. (2012). Meta-analysis and the development of knowledge. Perspectives on Psychological Science, 7, 79-92. [Fulltext][49] Davis‐Kean, P. E., & Sandler, H. M. (2001). A meta‐analysis of measures of self‐esteem for young children: A framework for future measures. Child development, 72(3), 887-906. [Fulltext][22] Eagly, A. H., & Wood, W. (1994). Using research syntheses to plan future research. In H. M. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 485-500). New York: Russell Sage Foundation. [Link][68] Smith, M. L., & Glass, G. V. (1977). Meta-analysis of psychotherapy outcome studies. American psychologist, 32, 752. [Fulltext][91] Tsuji, S., Bergmann, C., & Cristia, A. (2014). Community-Augmented Meta-Analyses Toward Cumulative Data Assessment. Perspectives on Psychological Science, 9, 661-665. [Link][88] Wood, W., & Eagly, A. H. (2009). Advantages of certainty and uncertainty. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds)., The handbook of research synthesis and meta-analysis (pp. 455-472). New York: Russell Sage. [Link][69] **Moderation and Mediation:** Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. London: Sage. Frazier, P.A., Tix, A.P., & Barron, K.E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115-134. [Fulltext][50] Kraemer, H. C., Kiernan, M., Essex, M., & Kupfer, D. J. (2008). How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychology, 27, S101-108. [Fulltext][77] Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equation models perform better than regressions. Journal of Consumer Psychology, 17, 140-154. [Fulltext][51] Ledgerwood, A., & Shrout, P. E. (2011). The tradeoff between accuracy and precision in latent variable models of mediation processes. Journal of Personality and Social Psychology, 101, 1174-1188. [Link][89] Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological methods, 18(2), 137. [Fulltext][52] **Multilevel Modeling:** Krull, J. L., & MacKinnon, D. P. (1999). Multilevel mediation modeling in group-based intervention studies. Evaluation Review, 23(4), 418-444. [Fulltext][76] Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate behavioral research, 36(2), 249-277. [Fulltext][75] McCoach, D. B., & Kaniskan, B. (2010). Using time-varying covariates in multilevel growth models. Frontiers in psychology, 1, 17. [Fulltext][53] 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(4), 323-355. [Fulltext][54] Yelland, L. N., Salter, A. B., Ryan, P., & Laurence, C. O. (2011). Adjusted intraclass correlation coefficients for binary data: methods and estimates from a cluster-randomized trial in primary care. Clinical Trials, 8(1), 48-58. [Link][55] **Multiple Regression:** Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological methods, 8(2), 129. [Fulltext][56] **Multivariate Statistics:** Tabachnik, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). Boston: Pearson. **Scale construction:** Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological assessment, 7, 309 - 319. [Fulltext][78] **Structural Equation Modeling:** Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal,2(2), 119-143. [Fulltext][57] Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological methods, 17(3), 354. [Fulltext][58] Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74. [Fulltext][59] **Survival Analysis:** Singer, J. D., & Willett, J. B. (1993). It’s about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational and Behavioral Statistics, 18(2), 155-195. [Fulltext][60] **Test theory:** Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. New York: Wadsworth. Embretson, S. E., & Reise, S. P. (2013). Item response theory for psychologists. Psychology Press. [Link][39] **Publication culture** Ledgerwood, A., & Sherman, J.W. (2012). Short, sweet, and problematic? The rise of the short report in psychological science. Perspectives on Psychological Science, 7, 60-66. [Link][61] **Reporting Practices:** Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer. Science, 345, 1502-1505. [Link][62] Franco, A., Simonovits, G. & Malhotra, N. (2015). Underreporting in political science survey experiments: Comparing questionnaires to published results. Political Analysis. [Link][63] Kashy, D. A., Donnellan, M. B., Ackerman, R. A., & Russell, D. W. (2009). Reporting and interpreting research in PSPB: Practices, principles, and pragmatics. Personality and Social Psychology Bulletin, 35, 1131-1142. [Fulltext][64] **III. BLOGS ABOUT METHODS AND STATISTICS** Dorothy Bishop. [BishopBlog][72]. Suzi Gage, Kate Button and others. [Sifting the Evidence][79]. [Jessica Hamrick.][86] Åse Kvist Innes-Ker. [Åse Fixes Science][73]. Deborah Mayo. [Error Statistics Philosophy][80]. Sophie Scott. [Speaking Out][85]. Bobbie Spellman. [My Perspectives (on PsychScience)][87] Simine Vazire. [sometimes i'm wrong][71]. [1]: [2]: [3]: [4]: [5]: [6]: [7]: [8]: [9]: [10]: [11]: [12]: [13]: [14]: [15]: [16]:;jsessionid=32A854E5EE635738432E978877EC84C1.f04t04?deniedAccessCustomisedMessage=&userIsAuthenticated=false [17]: [18]: [19]: [20]: [21]: [22]: [23]: [24]: [25]: [26]: [27]: [28]: [29]: [30]: [31]: [32]: [33]: [34]: [35]: [36]: [37]: [38]: [39]: [40]: [41]: [42]: [43]: [44]: [45]: [46]: [47]: [48]: [49]: [50]: [51]: [52]: [53]: [54]: [55]: [56]: [57]: [58]: [59]: [60]: [61]: [62]: [63]: [64]: [65]: [66]: [67]: [68]: [69]: [70]: [71]: [72]: [73]: [74]: [75]: [76]: [77]: [78]: [79]: [80]: [81]: [82]: [83]: [84]: [85]: [86]: [87]: [88]: [89]: [90]: [91]:
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