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Search and Selection Procedures of Literature Reviews in Behavior Analysis

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Abstract

Literature reviews allow professionals to identify effective interventions and assess developments in research and practice. As in other forms of scientific inquiry, the transparency of literature searches enhances the credibility of findings, particularly in regards to intervention research. The current review evaluated the characteristics of search methods employed in literature reviews appearing in publications concerning behavior analysis (n = 28) from 1997 to 2017. Specific aims included determining the frequency of narrative, systematic, and meta-analytic reviews over time; examining the publication of reviews in specific journals; and evaluating author reports of literature search and selection procedures. Narrative reviews (51.30%; n = 630) represented the majority of the total sample (n = 1,228), followed by systematic (31.51%; n = 387) and meta-analytic (17.18%; n = 211) reviews. In contrast to trends in related fields (e.g., special education), narrative reviews continued to represent a large portion of published reviews each year. The evaluated reviews exhibited multiple strengths; nonetheless, issues involving the reporting and execution of searches may limit the validity and replicability of literature reviews. A discussion of implications for research follows an overview of findings.

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References

  • American Psychological Association Presidential Task Force on Evidence-Based Practice. (2006). Evidence-based practice in psychology. The American Psychologist, 61(4), 271–285.

    Google Scholar 

  • Ator, N. A. (1999). Statistical inference in behavior analysis: Environmental determinants? Perspectives on Behavior Science, 22, 93–97.

    Google Scholar 

  • Baer, D. M. (1977). Perhaps it would be better not to know everything. Journal of Applied Behavior Analysis, 10, 167–172.

    PubMed  PubMed Central  Google Scholar 

  • Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1, 91–97.

    PubMed  PubMed Central  Google Scholar 

  • Baer, D. M., Wolf, M. M., & Risley, T. R. (1987). Some still-current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 20, 313–327.

    PubMed  PubMed Central  Google Scholar 

  • Baron, A., & Derenne, A. (2000). Quantitative summaries of single-subject studies: What do group comparisons tell us about individual performances? Perspectives on Behavior Science, 23, 101.

    Google Scholar 

  • Behrstock-Sherratt, E., Drill, K., & Miller, S. (2011). Is the supply in demand? Exploring how, when, and why teachers use research. Washington, DC: American Institutes for Research.

    Google Scholar 

  • Booth, A. (2010). How much searching is enough? Comprehensive versus optimal retrieval for technology assessments. International Journal of Technology Assessment in Health Care, 26, 431–435.

    PubMed  Google Scholar 

  • Buchanan, J., Husfeldt, J. D., Berg, T. M., & Houlihan, D. (2008). Publication trends in behavioral gerontology in the past 25 years: Are the elderly still an understudied population in behavioral research? Behavioral Interventions, 23, 65–74.

    Google Scholar 

  • Busacca, M. L., Anderson, A., & Moore, D. W. (2015). Self-management for primary school students demonstrating problem behavior in regular classrooms: Evidence review of single-case design research. Journal of Behavioral Education, 24(4), 373–401.

    Google Scholar 

  • Center, B. A., Skiba, R. J., & Casey, A. (1985). A methodology for the quantitative synthesis of intra-subject design research. Journal of Special Education, 19(4), 387–400.

    Google Scholar 

  • Chalmers, I., Hedges, L. V., & Cooper, H. (2002). A brief history of research synthesis. Evaluation & the Health Professions, 25, 12–37.

    Google Scholar 

  • Clarivate Analytics. (2017). 2016 journal citation reports® social sciences edition. Retrieved from https://jcr.clarivate.com/.

  • Cook, B. G. (2014). A call for examining replication and bias in special education research. Remedial & Special Education, 35(4), 233–246.

    Google Scholar 

  • Cook, B. G., Buysse, V., Klingner, J., Landrum, T. J., McWilliam, R. A., Tankersley, M., & Test, D. W. (2015). CEC's standards for classifying the evidence base of practices in special education. Remedial & Special Education, 36(4), 220–234.

    Google Scholar 

  • Cook, B. G., & Cook, S. C. (2013). Unraveling evidence-based practices in special education. Journal of Special Education, 47(2), 71–82.

    Google Scholar 

  • Cook, B. G., & Odom, S. L. (2013). Evidence-based practices and implementation science in special education. Exceptional Children, 79(2), 135–144.

    Google Scholar 

  • Cook, B. G., & Therrien, W. J. (2017). Null effects and publication bias in special education research. Behavioral Disorders, 42(4), 149–158

  • Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2009). The handbook of research synthesis and meta-analysis (2nd ed.). New York, NY: Russell Sage Foundation.

    Google Scholar 

  • Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper Saddle River, NJ: Pearson.

    Google Scholar 

  • Council for Exceptional Children. (2014). Council for Exceptional Children standards for evidence-based practices in special education. Retrieved from http://www.cec.sped.org/~/media/Files/Standards/Evidence%20based%20Practices%20and%20Practice/EBP%20FINAL.pdf.

  • Critchfield, T. S., Newland, C. M., & Kollins, S. H. (2000). The good, the bad, and the aggregate. Perspectives on Behavior Science, 23, 107–115.

    Google Scholar 

  • Cummings, P. (2011). Arguments for and against standardized mean differences (effect sizes). Archives of Pediatrics & Adolescent Medicine, 165(7), 592–596.

    Google Scholar 

  • Deeks, J. J., Higgins, P. T., & Altman, D. G. (2019). Analyzing data and undertaking meta-analyses. In J. P. T. Higgins, J. Thomas., J. Chandler, M. Cumpston, T. Li, M. J. Page, & V. A. Welch (Eds.), Cochrane handbook for systematic reviews of interventions version 6.0 (updated July 2019). Cochrane, 2019. Available from http://www.training.cochrane.org/handbook.

  • Delaney, A., & Tamás, P. A. (2018). Searching for evidence or approval? A commentary on database search in systematic reviews and alternative information retrieval methodologies. Research Synthesis Methods, 9(1), 124–131.

    PubMed  Google Scholar 

  • DeProspero, A., & Cohen, S. (1979). Inconsistent visual analysis of variance model for the intrasubject replication design. Journal of Applied Behavior Analysis, 12, 563–570.

    Google Scholar 

  • Derenne, A., & Baron, A. (1999). Human sensitivity to reinforcement: A comment on Kollins, Newland, and Critchfield’s (1997) quantitative literature review. Perspectives on Behavior Science, 22, 35–41.

    Google Scholar 

  • Dorsey, M. F., Weinberg, M., Zane, T., & Guidi, M. M. (2009). The case for licensure of applied behavior analysts. Behavior Analysis in Practice, 2(1), 53–58.

    PubMed  PubMed Central  Google Scholar 

  • Egger, M., Zellweger-Zähner, T., Schneider, M., Junker, C., Lengeler, C., & Antes, G. (1997). Language bias in randomized controlled trials published in English and German. The Lancet, 350(9074), 326–329.

    Google Scholar 

  • Ferguson, C. J., & Brannick, M. T. (2012). Publication bias in psychological science: prevalence, methods for identifying and controlling, and implications for the use of meta-analyses. Psychological Methods, 17(1), 120.

    PubMed  Google Scholar 

  • Fraley, L. E., & Vargas, E. A. (1986). Separate disciplines: The study of behavior and the study of the psyche. Perspectives on Behavior Science, 9, 47–59.

    Google Scholar 

  • Gage, N. A., Cook, B. G., & Reichow, B. (2017). Publication bias in special education meta-analyses. Exceptional Children, 83(4), 428–445.

    Google Scholar 

  • Galizio, M. (2020). JEAB: Past, present, and future. Journal of the Experimental Analysis of Behavior, 113, 3–7.

    PubMed  Google Scholar 

  • Gamba, J., Goyos, C., & Petursdottir, A. I. (2015). The functional independence of mands and tacts: Has it been demonstrated empirically? Analysis of Verbal Behavior, 31, 10–38.

    PubMed  Google Scholar 

  • Garg, A. X., Hackam, D., & Tonelli, M. (2008). Systematic review and meta-analysis: When one study is just not enough. Clinical Journal of the American Society of Nephrology, 3(1), 253–260.

    PubMed  Google Scholar 

  • Gingerich, W. J. (1984). Meta-analysis of applied time-series data. Journal of Applied Behavioral Science, 20, 71–79.

    PubMed  Google Scholar 

  • Graf, S. A. (1982). Is this the right road? A review of Kratochwill's single subject research: Strategies for evaluating change. Perspectives on Behavior Science, 5, 95.

    Google Scholar 

  • Hansen, H., & Trifkovic, N. (2013). Systematic reviews: Questions, methods and usage. Copenhagen, Denmark: Danish International Development Agency.

    Google Scholar 

  • Hantula, D., Critchfield, T. S., & Rasmussen, E. (2017). Swan song. Perspectives on Behavior Science, 40(2), 297–303.

    Google Scholar 

  • Hantula, D. A. (2016). Editorial: A very special issue. Perspectives on Behavior Science, 39, 1–5.

  • Hayes, S. C., Blackledge, J. T., & Barnes-Holmes. (2001). Language and cognition: Constructing an alternative approach with the behavioral tradition. In S. C. Hayes, D. Barnes-Holmes, & B. Roche (Eds.), Relational frame theory: A Post-Skinnerian account of human language and cognition (pp. 3–20). Cham, Switzerland: Springer.

  • Higgins, J. P., Altman, D. G., Gøtzsche, P. C., Jüni, P., Moher, D., Oxman, A. D., et al. (2011). The Cochrane Collaboration's tool for assessing risk of bias in randomized trials. BMJ, 343, d5928–d5928.

    PubMed  PubMed Central  Google Scholar 

  • Hojem, M. A., & Ottenbacher, K. J. (1988). Empirical investigation of visual-inspection versus trend-line analysis of single-subject data. Physical Therapy, 68(6), 983–988.

    PubMed  Google Scholar 

  • Horner, R. H., Carr, E. G., Halle, J., McGee, G., Odom, S., & Wolery, M. (2005). The use of single subject design research to identify evidence-based practices in special education. Exceptional Children, 71, 165–179.

    Google Scholar 

  • Ioannidis, J. P. (2016). The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses. The Milbank Quarterly, 94(3), 485–514.

  • Johnston, J. M., & Pennypacker, H. S. (2009). Strategies and tactics of behavioral research (3rd ed.). New York, NY: Routledge.

    Google Scholar 

  • Kahng, S., Hausman, N. L., Fisher, A. B., Donaldson, J. M., Cox, J. R., Lugo, M., & Wiskow, K. M. (2015). The safety of functional analyses of self-injurious behavior. Journal of Applied Behavior Analysis, 48(1), 107–114.

    PubMed  Google Scholar 

  • Kazdin, A. E. (2011). Single-case research designs: methods for clinical and applied settings (2nd ed.). New York, NY: Oxford University Press.

    Google Scholar 

  • Kennedy, C. H. (2005). Single-case designs for educational research. Boston, MA: Pearson.

    Google Scholar 

  • Killeen, P. R. (2019). Predict, control, and replicate to understand: How statistics can foster the fundamental goals of science. Perspectives on Behavior Science, 42, 109–132.

    PubMed  Google Scholar 

  • King, S., Davidson, K., Chitiyo, A., & Apple, D. (2020). Evaluating article search and selection procedures in special education literature reviews. Remedial & Special Education, 41, 3–17.

    Google Scholar 

  • Kollins, S. H., Newland, M. C., & Critchfield, T. S. (1997). Human sensitivity to reinforcement in operant choice: How much do consequences matter? Psychonomic Bulletin & Review, 4(2), 208–220.

    Google Scholar 

  • Kollins, S. H., Newland, M. C., & Critchfield, T. S. (1999). Quantitative integration of single-subject studies: Methods and misinterpretations. Perspectives on Behavior Science, 22, 149–157.

    Google Scholar 

  • Kostewicz, D., King, S., Datchuk, S., Brennan, K., & Casey, S. (2016). Data Collection and Measurement Assessment in Behavioral Research. Behavior Analysis: Research. Practice, 16(1), 19–33.

    Google Scholar 

  • Kratochwill, T. R., Levin, J. R., & Horner, R. H. (2018). Negative results: Conceptual and methodological dimensions in single- case intervention research. Remedial & Special Education, 39, 67–76.

    Google Scholar 

  • Kubina, R. M., Kostewicz, D. E., Brennan, K. M., & King, S. A. (2017). A critical review of line graphs in behavior analytic journals. Educational Psychology Review, 29(3), 583–598.

    Google Scholar 

  • Kyonka, E. G., Mitchell, S. H., & Bizo, L. A. (2019). Beyond inference by eye: Statistical and graphing practices in JEAB, 1992–2017. Journal of the Experimental Analysis of Behavior, 111, 155–165.

    PubMed  Google Scholar 

  • Lanovaz, M. J., & Rapp, J. T. (2016). Using single-case experiments to support evidence-based decisions: How much is enough? Behavior Modification, 40, 377–395.

    PubMed  Google Scholar 

  • Lanovaz, M. J., Turgeon, S., Cardinal, P., & Wheatley, T. L. (2019). Using single-case designs in practical settings: Is within-subject replication always necessary? Perspectives on Behavior Science, 42, 153–162.

    PubMed  Google Scholar 

  • Laraway, S., Snycerski, S., Pradhan, S., & Huitema, B. E. (2019). An overview of scientific reproducibility: Consideration of relevant issues for behavior science/analysis. Perspectives on Behavior Science, 42, 33–57.

    PubMed  PubMed Central  Google Scholar 

  • Lawrence, D. W. (2008). What is lost when searching only one literature database for articles relevant to injury prevention and safety promotion? Injury Prevention, 14(6), 401–404.

    PubMed  Google Scholar 

  • Ledford, J. R., King, S. A., Harbin, E. R., & Zimmerman, K. N. (2018). Antecedent social skills interventions for individuals with ASD: What works, for whom, and under what conditions? Focus on Autism & Other Developmental Disabilities, 33(1), 3–13.

    Google Scholar 

  • Lemons, C. J., King, S. A., Davidson, K. A., Berryessa, T. L., Gajjar, S. A., & Sacks, L. H. (2016). An inadvertent concurrent replication: Same roadmap, different journey. Remedial & Special Education, 37(4), 213–222.

    Google Scholar 

  • Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. Journal of Clinical Epidemiology, 62, e1–e34.

    PubMed  Google Scholar 

  • Littell, J. H., & Girvin, H. (2002). Stages of change: A critique. Behavior Modification, 26, 223–273.

    PubMed  Google Scholar 

  • Mace, F. C., & Critchfield, T. S. (2010). Translational research in behavior analysis: Historical traditions and imperative for the future. Journal of the Experimental Analysis of Behavior, 93, 293–312.

    PubMed  PubMed Central  Google Scholar 

  • Mackay, H. C., Barkham, M., Rees, A., & Stiles, W. B. (2003). Appraisal of published reviews of research on psychotherapy and counseling with adults, 1990–1998. Journal of Consulting & Clinical Psychology, 71(4), 652.

    Google Scholar 

  • Maggin, D. M., Chafouleas, S. M., Goddard, K. M., & Johnson, A. H. (2011b). A systematic evaluation of token economies as a classroom management tool for students with challenging behavior. Journal of School Psychology, 49(5), 529–554.

    PubMed  Google Scholar 

  • Maggin, D. M., O'Keeffe, B. V., & Johnson, A. H. (2011a). A quantitative synthesis of methodology in the meta-analysis of single-subject research for students with disabilities: 1985–2009. Exceptionality, 19, 109–135.

    Google Scholar 

  • Maggin, D. M., Talbott, E., Van Acker, E. Y., & Kumm, S. (2017). Quality indicators for systematic reviews in behavioral disorders. Behavioral Disorders, 42(2), 52–64.

    Google Scholar 

  • Mahood, Q., Van Eerd, D., & Irvin, E. (2014). Searching for grey literature for systematic reviews: Challenges and benefits. Research Synthesis Methods, 5, 221–234.

    PubMed  Google Scholar 

  • Maner, J. K. (2014). Let’s put our money where our mouth is if authors are to change their ways, reviewers (and editors) must change with them. Perspectives on Psychological Science, 9(3), 343–351.

    PubMed  Google Scholar 

  • Manolov, R., Losada, J. L., Chacón-Moscoso, S., & Sanduvete-Chaves, S. (2016). Analyzing two-phase single-case data with non-overlap and mean difference indices: illustration, software tools, and alternatives. Frontiers in Psychology, 7, 1–16.

    Google Scholar 

  • Manolov, R., & Vannest, K. J. (2019). A visual aid and objective rule encompassing the data features of visual analysis. Behavior Modification. https://doi.org/10.1177/0145445519854323.

  • Marr, M. J. (2017). The future of behavior analysis: Foxes and hedgehogs revisited. Perspectives on Behavior Science, 40(1), 197–207.

    Google Scholar 

  • Martin, N. T., Nosik, M. R., & Carr, J. E. (2016). International publication trends in the journal of applied behavior analysis: 2000–2014. Journal of Applied Behavior Analysis, 49(2), 416–420.

    PubMed  Google Scholar 

  • McSweeney, F. K., & Swindell, S. (1998). Women in the experimental analysis of behavior. Perspectives on Behavior Science, 21(2), 193–202.

    Google Scholar 

  • Miller, F. G., & Lee, D. L. (2013). Do functional behavioral assessments improve intervention effectiveness for students diagnosed with ADHD? A single-subject meta-analysis. Journal of Behavioral Education, 22, 253–282.

    Google Scholar 

  • Moher, D., Cook, D. J., Eastwood, S., Olkin, I., Rennie, D., & Stroup, D. F. (1999). Improving the quality of reports of meta-analyses of randomized controlled trials: The QUOROM statement. The Lancet, 354(9193), 1896–1900.

    Google Scholar 

  • Moher, D., Tetzlaff, J., Tricco, A. C., Sampson, M., & Altman, D. G. (2007). Epidemiology and reporting characteristics of systematic reviews. PLoS Medicine, 4(3), e78.

    PubMed  PubMed Central  Google Scholar 

  • Moore, T. C., Maggin, D. M., Thompson, K. M., Gordon, J. R., Daniels, S., & Lang, L. E. (2019). Evidence review for teacher praise to improve students’ classroom behavior. Journal of Positive Behavior Interventions, 21(1), 3–18.

    Google Scholar 

  • Morris, E. K., Altus, D. E., & Smith, N. G. (2013). A study in the founding of applied behavior analysis through its publications. Perspectives on Behavior Science, 36(1), 73–107.

    Google Scholar 

  • Nickerson, R. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5(2), 241–301.

    PubMed  Google Scholar 

  • Ninci, J., Vannest, K. J., Wilson, V., & Zhang, N. (2015). Interrater agreement between visual analysts of single-case data: a meta-analysis. Behavior Modification, 39, 510–541. https://doi.org/10.1177/0145445515581327.

    Article  PubMed  Google Scholar 

  • Odom, S. L. (2009). The tie that binds: Evidence-based practice, implementation science, and outcomes for children. Topics in Early Childhood Special Education, 29(1), 53–61.

    Google Scholar 

  • Parker, R. I., Vannest, J. K., & Davis, J. L. (2011). Effect size in single-case research: A review of nine nonoverlap techniques. Behavior Modification, 35, 303–322.

    PubMed  Google Scholar 

  • Pennypacker, H. S. (2012). Evidence reconsidered. European Journal of Behavior Analysis, 13(1), 83–86.

    Google Scholar 

  • Perone, M. (1999). Statistical inference in behavior analysis: Experimental control is better. Perspectives on Behavior Science, 22, 109–116.

    Google Scholar 

  • Perone, M. (2019). How I learned to stop worrying and love replication failures. Perspectives on Behavior Science, 42(1), 91–108.

    PubMed  Google Scholar 

  • Petticrew, M. (2015). Time to rethink the systematic review catechism? Moving from “what works” to “what happens”. Systematic Reviews, 1(4), 1–6.

    Google Scholar 

  • Petticrew, M., & Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. Malden, MA: Blackwell.

    Google Scholar 

  • Petursdottir, A. I., & Carr, J. E. (2018). Applying the taxonomy of validity threats from mainstream research design to single-case experiments in applied behavior analysis. Behavior Analysis in Practice, 11(3), 228–240.

    PubMed  PubMed Central  Google Scholar 

  • Polanin, J. R., Tanner-Smith, E. E., & Hennessy, E. A. (2016). Estimating the difference between published and unpublished effect sizes: A meta-review. Review of Educational Research, 86, 207–236.

    Google Scholar 

  • Pustejovsky, J. E. (2015). Effects of measurement operation on the magnitude of nonoverlap effect sizes for single-case experimental designs. Paper presented at the 2015 annual meeting of the American Educational Research Association. Chicago, Illinois; April 15-April 20.

  • Pustejovsky, J. E., & Ferron, J. M. (2017). Research synthesis and meta-analysis of single-case designs. In J. M. Kaufmann, D. P. Hallahan, & P. C. Pullen (Eds.), Handbook of special education (2nd ed.). New York, NY: Routledge. pp. 168–186

  • Salzberg, C. L., Strain, P. S., & Baer, D. M. (1987). Meta-analysis for single-subject research: When does it clarify, when does it obscure? Remedial & Special Education, 8, 43–48.

    Google Scholar 

  • Sampson, M., McGowan, J., Cogo, E., Grimshaw, J., Moher, D., & Lefebvre, C. (2009). An evidence-based practice guideline for the peer review of electronic search strategies. Journal of Clinical Epidemiology, 62(9), 944–952.

    PubMed  Google Scholar 

  • Schlichenmeyer, K. J., Roscoe, E. M., Rooker, G. W., Wheeler, E. E., & Dube, W. V. (2013). Idiosyncratic variables that affect functional analysis outcomes: A review (2001–2010). Journal of Applied Behavior Analysis, 46(1), 339–348.

    PubMed  PubMed Central  Google Scholar 

  • Scruggs, T. E., Mastropieri, M. A., & Casto, G. (1987a). The quantitative synthesis of single-subject research: Methodology and validation. Remedial & Special Education, 8, 24–33.

    Google Scholar 

  • Scruggs, T. E., Mastropieri, M. A., & Casto, G. (1987b). Response to Salzberg, Strain, and Baer. Remedial & Special Education, 8, 49–52.

  • Scruggs, T. E., Mastropieri, M. A., Cook, S. B., & Escobar, C. (1986). Early intervention for children with conduct disorders: A quantitative synthesis of single-subject research. Behavioral Disorders, 11, 260–271.

    Google Scholar 

  • Seubert, C., Fryling, M. J., Wallace, M. D., Jiminez, A. R., & Meier, A. E. (2014). Antecedent interventions for pediatric feeding problems. Journal of Applied Behavior Analysis, 47, 449–453.

    PubMed  Google Scholar 

  • Shadish, W.R., Hedges, L.V., Horner, R.H., and Odom, S.L. (2015). The role of between-case effect size in conducting, interpreting, and summarizing single-case research. (NCER 2015-002) Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ies.ed.gov/

  • Shadish, W. R., Zelinsky, N. A., Vevea, J. L., & Kratochwill, T. R. (2016). A survey of publication practices of single-case design researchers when treatments have small or large effects. Journal of Applied Behavior Analysis, 49, 656–673.

    PubMed  Google Scholar 

  • Shahan, T. A. (2010). Conditioned reinforcement and response strength. Journal of the Experimental Analysis of Behavior, 93(2), 269–289.

    PubMed  PubMed Central  Google Scholar 

  • Sham, E., & Smith, T. (2014). Publication bias in studies of an applied behavior-analytic intervention: An initial analysis. Journal of Applied Behavior Analysis, 47(3), 663–678.

    PubMed  Google Scholar 

  • Sharpe, D. (1997). Of apples and oranges, file drawers and garbage: Why validity issues in meta-analysis will not go away. Clinical Psychology Review, 17(8), 881–901.

  • Shea, B., Dubé, C., & Moher, D. (2001). Assessing the quality of reports of systematic reviews: The QUOROM statement compared to other tools. In M. Egger, G. D. Smith, & D. G. Altman (Eds.), Systematic reviews in health care: Meta-analysis in context (2nd ed., pp. 122–139). London, UK: BMJ Publishing.

    Google Scholar 

  • Sidman, M. (1960). Tactics of scientific research: Evaluating experimental data in psychology. New York, NY: Basic Books.

    Google Scholar 

  • Siontis, K. C., Hernandez-Boussard, T., & Ioannidis, J. P. (2013). Overlapping meta-analyses on the same topic: survey of published studies. BMJ, 347. https://doi.org/10.1136/bmj.f4501

  • Skinner, B. F. (1938). The behavior of organisms: An experimental analysis. New York, NY: Appleton-Century-Crofts.

    Google Scholar 

  • Skinner, B. F. (1956). A case history in scientific method. American Psychologist, 11, 221–233.

    Google Scholar 

  • Slavin, R. E. (1995). Best evidence synthesis: An intelligent alternative to meta-analysis. Journal of Clinical Epidemiology, 48(1), 9–18.

    PubMed  Google Scholar 

  • Slocum, T. A., Detrich, R., Wilczynski, S. M., Spencer, T. D., Lewis, T., & Wolfe, K. (2014). The evidence-based practice of applied behavior analysis. Perspectives on Behavior Science, 37(1), 41–56.

    Google Scholar 

  • Talbott, E., Maggin, D. M., Van Acker, E. Y., & Kumm, S. (2018). Quality indicators for reviews of research in special education. Exceptionality, 26(4), 245–265.

    Google Scholar 

  • Tarlow, K. R. (2017). An improved rank correlation effect size statistic for single-case designs: Baseline corrected Tau. Behavior Modification, 41(4), 427–467.

    PubMed  Google Scholar 

  • Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., et al. (2011). Data sharing by scientists: Practices and perceptions. PLoS ONE, 6(6), e21101.

    PubMed  PubMed Central  Google Scholar 

  • Therrien, W. J., Mathews, H. M., Hirsch, S. E., & Solis, M. (2016). Progeny review: An alternative approach for examining the replication of intervention studies in special education. Remedial & Special Education, 37, 235–243. https://doi.org/10.1177/0741932516646081.

    Article  Google Scholar 

  • Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, S. W. (2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children, 71, 181–194.

    Google Scholar 

  • Thorlund, K., Druyts, E., Aviña-Zubieta, J. A., Wu, P., & Mills, E. J. (2013). Why the findings of published multiple treatment comparison meta-analyses of biologic treatments for rheumatoid arthritis are different: an overview of recurrent methodological shortcomings. Annals of the Rheumatic Diseases, 72(9), 1524–1535.

    PubMed  Google Scholar 

  • Tincani, M., & Travers, J. (2019). Replication research, publication bias, and applied behavior analysis. Perspectives on Behavior Science, 42(1), 59–75.

    PubMed  PubMed Central  Google Scholar 

  • Valentine, J. C., Cooper, H. M., Patall, E. A., Tyson, D., & Robinson, J. C. (2010). A method for evaluating research syntheses: The quality, conclusions, and consensus of 12 syntheses of the effects of after-school programs. Research Synthesis Methods, 1(1), 20–23.

    PubMed  Google Scholar 

  • Vargas, E. A. (1987). "Separate disciplines" is another name for survival. Perspectives on Behavior Science, 10(1), 119–121.

    Google Scholar 

  • Vollmer, T. R., Hagopian, L. P., Bailey, J. S., Dorsey, M. F., Hanley, G. P., Lennox, D., et al. (2011). The Association for Behavior Analysis International position statement on restraint and seclusion. Perspectives on Behavior Science, 34(1), 103.

    Google Scholar 

  • Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10, 365–391.

  • Wang, S., Parilla, R., & Cui, Y. (2013). Meta-analysis of social skills interventions of single-case research for individuals with autism spectrum disorders: Results from three-level HLM. Journal of Autism & Developmental Disorders, 43, 1701–1716.

    Google Scholar 

  • Wang, Q., & Waltman, L. (2016). Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus. Journal of Informetrics, 10, 347–364.

  • Web of Science (WOS). (2017). 2016 journal citation reports® social sciences edition. Thompson Reuters. Retrieved from https://jcr-incites-thomsonreuters-com.

  • Weisz, J. R., & Hawley, K. M. (2002). Procedural and coding manual for identification of beneficial treatments. Washington, DC: American Psychological Association, Society for Clinical Psychology, Division 12.

  • Wendt, O., & Miller, B. (2012). Quality appraisal of single-subject experimental designs: An overview and comparison of different appraisal tools. Education & Treatment of Children, 35, 235–268.

    Google Scholar 

  • What Works Clearinghouse. (2017). Standards handbook (Version 4.0). Author. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_standards_handbook_v4.pdf.

  • Wolery, M., Busick, M., Reichow, B., & Barton, E. E. (2010). Comparison of overlap methods for quantitatively synthesizing single-subject data. Journal of Special Education, 44, 18–28.

    Google Scholar 

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King, S.A., Kostewicz, D., Enders, O. et al. Search and Selection Procedures of Literature Reviews in Behavior Analysis. Perspect Behav Sci 43, 725–760 (2020). https://doi.org/10.1007/s40614-020-00265-9

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