Author
David C. Cicero
Other affiliations: University of Missouri, University of Hawaii at Manoa, University of Louisville ...read more
Bio: David C. Cicero is an academic researcher from University of North Texas. The author has contributed to research in topics: Schizotypy & Measurement invariance. The author has an hindex of 25, co-authored 78 publications receiving 2456 citations. Previous affiliations of David C. Cicero include University of Missouri & University of Hawaii at Manoa.
Papers published on a yearly basis
Papers
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Richard A. Klein1, Michelangelo Vianello2, Fred Hasselman3, Byron G. Adams4 +187 more•Institutions (118)
TL;DR: This paper conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings, and found that very little heterogeneity was attributable to the order in which the tasks were performed or whether the task were administered in lab versus online.
Abstract: We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance (p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion (p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.
495 citations
University of Minnesota1, Stony Brook University2, University of Notre Dame3, Macquarie University4, University of North Texas5, University at Buffalo6, University of Kentucky7, University of Vermont8, University of Toronto9, University of South Florida10, University of Maryland, Baltimore11, Southern Methodist University12, University of Hawaii13, College of William & Mary14, Ghent University15, University of Utah16, University of Michigan17, Columbia University18, University of Kansas19, Pennsylvania State University20, University of California, Davis21, Georgia State University22, University of Iowa23, University of Georgia24, Texas A&M University25, Oklahoma State University–Stillwater26, University of Groningen27, University of Liverpool28, Florida State University29, Uniformed Services University of the Health Sciences30, Maastricht University31, Bryn Mawr College32, Purdue University33, University of Otago34, University of Maryland, College Park35, University of Arizona36, University of New South Wales37, Northwestern University38, Emory University39, Oak Ridge National Laboratory40, University of Pittsburgh41, Vanderbilt University42
TL;DR: The aims and current foci of the HiTOP Consortium, a group of 70 investigators working together to study empirical classification of psychopathology, are described, which pertain to continued research on the empirical organization of psychopathological constructs; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic.
308 citations
University of Virginia1, University of California, Davis2, Miami University3, Montana State University4, Nova Southeastern University5, University of California, Riverside6, Pennsylvania State University7, Ashland University8, University of North Florida9, Virginia Commonwealth University10, Carleton University11, University of Hawaii at Manoa12, University of Florida13, Texas A&M University14, San Diego State University15, University of Southern Mississippi16, Pacific Lutheran University17, Bradley University18, Michigan State University19, University of Toronto20, Ithaca College21, Ohio State University22
TL;DR: This paper examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools and with an online sample.
270 citations
College of William & Mary1, Macquarie University2, University of Kansas3, University of Amsterdam4, Pennsylvania State University5, University at Albany, SUNY6, Oklahoma State University–Stillwater7, University of Maryland, College Park8, University of Arizona9, Purdue University10, University of New South Wales11, Vanderbilt University12, Université de Montréal13, University of South Florida14, University of Utah15, University of Minnesota16, University of Liverpool17, Northwestern University18, King's College London19, Maastricht University20, Emory University21, University of Pittsburgh22, University of Kassel23, University of Toronto24, Southern Methodist University25, University of Hawaii at Manoa26, University of Notre Dame27, Medical Research Council28, University of California, Davis29, University of Vermont30, Georgia State University31, Florida State University32, University of North Texas33, Stony Brook University34
TL;DR: The Hierarchical Taxonomy of Psychopathology (HiTOP) as discussed by the authors is based on empirical patterns of co-occurrence among psychological symptoms, and it has the potential to accelerate and improve research on mental health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
Abstract: For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
225 citations
University of California, Davis1, Stony Brook University2, University of Minnesota3, University of Notre Dame4, University of Kentucky5, University of Vermont6, Syracuse University7, Region Zealand8, University of Toronto9, Harvard University10, University of South Florida11, Southern Methodist University12, University of Hawaii at Manoa13, College of William & Mary14, Ghent University15, University of Utah16, Texas A&M University17, University of Kansas18, Zürcher Fachhochschule19, Dresden University of Technology20, University of British Columbia21, Albany Medical College22, Purdue University23, University of Iowa24, University of Georgia25, Oklahoma State University–Stillwater26, University of Groningen27, Florida State University28, Pennsylvania State University29, University of North Texas30, University of Otago31, University of New South Wales32, Northwestern University33, University of Missouri34, McGill University35, Emory University36, University of Tennessee37, University of Pittsburgh38, Marian University39, Vanderbilt University40
TL;DR: Author(s): Hopwood, Christopher J; Kotov, Roman; Krueger, Robert F; Watson, David; Widiger, Thomas A; Widinger,Thomas A; Althoff, Robert R; Ansell, Emily B; Bach, Bo; Michael Bagby, R; Blais, Mark A; Bornovalova, Marina A; Chmielewski, Michael; Cicero, David C; Conway, Christopher; De Clercq, Barbara;
Abstract: Author(s): Hopwood, Christopher J; Kotov, Roman; Krueger, Robert F; Watson, David; Widiger, Thomas A; Althoff, Robert R; Ansell, Emily B; Bach, Bo; Michael Bagby, R; Blais, Mark A; Bornovalova, Marina A; Chmielewski, Michael; Cicero, David C; Conway, Christopher; De Clercq, Barbara; De Fruyt, Filip; Docherty, Anna R; Eaton, Nicholas R; Edens, John F; Forbes, Miriam K; Forbush, Kelsie T; Hengartner, Michael P; Ivanova, Masha Y; Leising, Daniel; John Livesley, W; Lukowitsky, Mark R; Lynam, Donald R; Markon, Kristian E; Miller, Joshua D; Morey, Leslie C; Mullins-Sweatt, Stephanie N; Hans Ormel, J; Patrick, Christopher J; Pincus, Aaron L; Ruggero, Camilo; Samuel, Douglas B; Sellbom, Martin; Slade, Tim; Tackett, Jennifer L; Thomas, Katherine M; Trull, Timothy J; Vachon, David D; Waldman, Irwin D; Waszczuk, Monika A; Waugh, Mark H; Wright, Aidan GC; Yalch, Mathew M; Zald, David H; Zimmermann, Johannes
196 citations
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TL;DR: This work argues for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives, in the hope that this will facilitate action toward improving the transparency, reproducible and efficiency of scientific research.
Abstract: Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.
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