The importance of covariate selection in controlling for selection bias in observational studies.

Cited by (483)

Items 401–483 of 483

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  18. The Sensitivity of Small Area Estimates under Propensity Score Subclassification for Generalization
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  19. Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator
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  21. Preface
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  22. Construct and Data-Evaluation Validity
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  23. Single-Case Experimental Research Designs
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  24. Control and Comparison Groups
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  25. Cautions, Negative Effects, and Replication
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  26. Selecting Measures for Research
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  27. An Empirical Validation of the Regression Point Displacement Design Using Within-Study Comparison Logic: Emerging Possibilities and Cautions
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  28. Glossary
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  29. Special Topics of Assessment
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  30. References
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  31. Ideas that Begin the Research Process
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  32. Methodology
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  33. Subject Index
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  34. Presenting and Analyzing the Data
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  35. Name Index
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  36. Scientific Integrity
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  37. Introduction
    Alan E. Kazdin · 2021 · Cambridge University Press eBooks
  38. Assessment: Types of Measures and Their Use
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  39. Experimental Research Using Group Designs
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  40. Ethical Issues and Guidelines for Research
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  41. Communication of Research Findings
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  47. Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator
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  54. Class Enumeration and Parameter Bias in Growth Mixture Models with Misspecified Time-Varying Covariates: A Monte Carlo Simulation Study
    Jayme M. Palka · 2020 · University of North Texas Digital Library (University of North Texas)
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  64. Effectiveness of Propensity Score Methods in a Multilevel Framework: A Monte Carlo Study
    Aarti P. Bellara · 2013 · Digital Commons - University of South Florida (University of South Florida)
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  69. Propensity Score Methods in Quasi‐Experimental Research
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