Harmonizing Social, Emotional, and Behavioral Constructs in Prevention Science: Digging into the Weeds of Aligning Disparate Measures

[1]  E. Westling,et al.  Long-term Effects of the Family Check-Up on Suicidality in Childhood and Adolescence: Integrative Data Analysis of Three Randomized Trials , 2022, Prevention Science.

[2]  K. Mills,et al.  Estimating posttraumatic stress disorder severity in the presence of differential item functioning across populations, comorbidities, and interview measures: Introduction to Project Harmony. , 2022, Journal of traumatic stress.

[3]  J. Lochman,et al.  Design and methodology for an integrative data analysis of coping power: Direct and indirect effects on adolescent suicidality. , 2022, Contemporary clinical trials.

[4]  John C. Lin,et al.  Pre-statistical harmonization of behavrioal instruments across eight surveys and trials , 2021, BMC Medical Research Methodology.

[5]  Stephen J Tueller,et al.  Mindful Coping Power: Comparative Effects on Children’s Reactive Aggression and Self-Regulation , 2021, Brain sciences.

[6]  Daniel J. Bauer,et al.  Psychometric models for scoring multiple reporter assessments: Applications to integrative data analysis in prevention science and beyond , 2020, International journal of behavioral development.

[7]  J. Lochman,et al.  Theoretical Model of Mindful Coping Power: Optimizing a Cognitive Behavioral Program for High-Risk Children and Their Parents by Integrating Mindfulness , 2020, Clinical Child and Family Psychology Review.

[8]  Catherine P. Bradshaw,et al.  Both/and: Tier 2 Interventions with Transdiagnostic Utility in Addressing Emotional and Behavioral Disorders in Youth , 2020 .

[9]  T. Killeen,et al.  Estimation of equable scale scores and treatment outcomes from patient- and clinician-reported PTSD measures using item response theory calibration. , 2019, Psychological assessment.

[10]  Ahnalee M. Brincks,et al.  Evaluating construct equivalence of youth depression measures across multiple measures and multiple studies. , 2019, Psychological assessment.

[11]  Jimmy de la Torre,et al.  Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies , 2019, Front. Psychol..

[12]  Tuppett M. Yates,et al.  Data Harmonization: Establishing Measurement Invariance across Different Assessments of the Same Construct across Adolescence , 2019, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.

[13]  Daniel J. Bauer,et al.  Advancing the Study of Adolescent Substance Use Through the Use of Integrative Data Analysis , 2018, Evaluation & the health professions.

[14]  G. Howe,et al.  Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials , 2018, Prevention Science.

[15]  T. Perrino,et al.  Programs for Preventing Depression in Adolescence: Who Benefits and Who Does Not? An Introduction to the Supplemental Issue , 2018, Prevention Science.

[16]  Daniel J Bauer,et al.  A More General Model for Testing Measurement Invariance and Differential Item Functioning , 2017, Psychological methods.

[17]  J. Lochman,et al.  Testing the feasibility of a briefer school-based preventive intervention with aggressive children: A hybrid intervention with face-to-face and internet components. , 2017, Journal of school psychology.

[18]  E. R. van den Heuvel,et al.  Comparison of Standardization Methods for the Harmonization of Phenotype Data: An Application to Cognitive Measures. , 2016, American journal of epidemiology.

[19]  Parminder Raina,et al.  Maelstrom Research guidelines for rigorous retrospective data harmonization , 2016, International journal of epidemiology.

[20]  E. Reider,et al.  Family-Based Preventive Interventions: Can the Onset of Suicidal Ideation and Behavior Be Prevented? , 2016, Suicide & life-threatening behavior.

[21]  John D Potter,et al.  Toward Rigorous Data Harmonization in Cancer Epidemiology Research: One Approach. , 2015, American journal of epidemiology.

[22]  Su-Young Kim,et al.  A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis , 2015, Psychometrika.

[23]  J. Lochman,et al.  Evidence-based preventive intervention for preadolescent aggressive children: One-year outcomes following randomization to group versus individual delivery. , 2015, Journal of consulting and clinical psychology.

[24]  David C. Atkins,et al.  Project INTEGRATE: An integrative study of brief alcohol interventions for college students. , 2015, Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors.

[25]  Parminder Raina,et al.  Statistical approaches to harmonize data on cognitive measures in systematic reviews are rarely reported. , 2015, Journal of clinical epidemiology.

[26]  Daniel J Bauer,et al.  A Moderated Nonlinear Factor Model for the Development of Commensurate Measures in Integrative Data Analysis , 2014, Multivariate behavioral research.

[27]  Ruth E Baldasaro,et al.  A trifactor model for integrating ratings across multiple informants. , 2013, Psychological methods.

[28]  J. Lochman,et al.  Three Year Follow-Up of Coping Power Intervention Effects: Evidence of Neighborhood Moderation? , 2013, Prevention Science.

[29]  Gracelyn Cruden,et al.  Advancing Science Through Collaborative Data Sharing and Synthesis , 2013, Perspectives on psychological science : a journal of the Association for Psychological Science.

[30]  Daniel J Bauer,et al.  Integrative data analysis in clinical psychology research. , 2013, Annual review of clinical psychology.

[31]  J. Lochman,et al.  Coping Power Dissemination Study: Intervention and Special Education Effects on Academic Outcomes , 2012 .

[32]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[33]  Andrea M Hussong,et al.  Integrative data analysis: the simultaneous analysis of multiple data sets. , 2009, Psychological methods.

[34]  J. Lochman,et al.  Dissemination of the Coping Power program: importance of intensity of counselor training. , 2009, Journal of consulting and clinical psychology.

[35]  Kevin J. Grimm,et al.  Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement. , 2009, Psychological methods.

[36]  Daniel J Bauer,et al.  Psychometric approaches for developing commensurate measures across independent studies: traditional and new models. , 2009, Psychological methods.

[37]  Aribert Rothenberger,et al.  Multicultural assessment of child and adolescent psychopathology with ASEBA and SDQ instruments: research findings, applications, and future directions. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[38]  D. Roth,et al.  Masked intervention effects: Analytic methods for addressing low dosage of intervention , 2006 .

[39]  T. S. Ragu-Nathan,et al.  The Q-Sort Method: Assessing Reliability And Construct Validity Of Questionnaire Items At A Pre-Testing Stage , 2002 .

[40]  C. Edelbrock,et al.  The classification of child psychopathology: a review and analysis of empirical efforts. , 1978, Psychological bulletin.

[41]  D. Campbell,et al.  Convergent and discriminant validation by the multitrait-multimethod matrix. , 1959, Psychological bulletin.

[42]  J. Lochman,et al.  Does a Booster Intervention Augment the Preventive Effects of an Abbreviated Version of the Coping Power Program for Aggressive Children? , 2014, Journal of abnormal child psychology.

[43]  R. King,et al.  Suicidal behavior and violence in male adolescents: a school-based study. , 2003, Journal of the American Academy of Child and Adolescent Psychiatry.

[44]  T. Achenbach,et al.  The classification of children's psychiatric symptoms: a factor-analytic study. , 1966, Psychological monographs.