The "Goldilocks Zone": (Too) Many Confidence Intervals In Tests of Mediation Just Exclude Zero

被引:36
|
作者
Gotz, Martin [1 ]
O'Boyle, Ernest H. [2 ]
Gonzalez-Mule, Erik [2 ]
Banks, George C. [3 ]
Bollmann, Stella S. [4 ]
机构
[1] Univ Zurich, Dept Psychol, Zurich, Switzerland
[2] Indiana Univ, Kelley Sch Business, Dept Management & Entrepreneurship, 1309 East 10th St, Bloomington, IN 47405 USA
[3] Univ North Carolina Charlotte, Belk Coll Business, Dept Management, Charlotte, NC USA
[4] Univ Teacher Educ Lucerne, Inst Divers Educ, Luzern, Switzerland
关键词
philosophy of science; questionable research practices; mediation; null results; QUESTIONABLE RESEARCH PRACTICES; CROSS-SECTIONAL ANALYSES; SAMPLE-SIZE; PUBLICATION BIAS; P VALUES; PSYCHOLOGY; MODELS; POWER; RECOMMENDATIONS; PREVALENCE;
D O I
10.1037/bul0000315
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Questionable research practices (QRPs) can occur whenever one result is favored over another, and tests of mediation are no exception. Given mediation's ubiquity and importance to both theory and practice, QRPs in tests of mediation pose a serious threat to the advancement of psychology. We investigate this issue through the introduction of a straightforward means of detecting the presence and magnitude of QRPs in tests of mediation and validate this methodology with a series of sensitivity tests and simulations. We then apply this method to 2,569 tests of mediation published in five leading psychology journals in 2018 and 2019. We find that despite most hypothesized tests of mediation being likely underpowered. most (76%) were nevertheless supported. Furthermore, confidence intervals (CIs) that just barely exclude zero are 3.6 to 4.4 times as prevalent as those CI's that just barely include zero. We also find a number of study- and test-level factors, such as whether the test of mediation was hypothesized, explain both whether the CI excluded zero (odds ratio [OR] = 17.87,p < .001) as well as the CI's proximity to zero (b = .27, p < .001). In addition, other factors, most notably sample size, do predict the CT's proximity to zero (gamma = .00, p < .001), but surprisingly do not predict the CT's exclusion of zero (OR = .99. p = .803). We conclude with actionable QRP curtailment strategies so that both, academics and practitioners, can have greater and well-founded confidence in tests of mediation in psychological research.
引用
收藏
页码:95 / 114
页数:20
相关论文
共 9 条
  • [1] Goldilocks in the ICU: Too Few Beds, Too Many, or Just Right?
    Damuth, Emily
    Schorr, Christa A.
    [J]. CRITICAL CARE MEDICINE, 2013, 41 (12) : 2820 - 2821
  • [2] Too Little, Too Much, and "Just Right": Exploring the "Goldilocks Zone" of Daily Stress Reactivity
    Rush, Jonathan
    Ong, Anthony D.
    Piazza, Jennifer R.
    Charles, Susan T.
    Almeida, David M.
    [J]. EMOTION, 2024, 24 (05) : 1249 - 1258
  • [3] Construction of confidence intervals for the mean of a population containing many zero values
    Kvanli, AH
    Shen, YK
    Deng, LY
    [J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 1998, 16 (03) : 362 - 368
  • [4] Tests and Confidence Intervals for the Mean of a Zero-Inflated Poisson Distribution
    Waguespack D.
    Krishnamoorthy K.
    Lee M.
    [J]. American Journal of Mathematical and Management Sciences, 2020, 39 (04): : 383 - 390
  • [5] Empirical likelihood confidence intervals for the mean of a population containing many zero values
    Chen, JH
    Chen, SY
    Rao, JNK
    [J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2003, 31 (01): : 53 - 68
  • [6] Goldilocks and the importance of AV intervals in cardiac resynchronization-How to best find the AV interval that is not too long, not too short, but just right for patients
    Verdino, Ralph J.
    [J]. HEART RHYTHM, 2013, 10 (08) : 1144 - 1145
  • [7] Modified empirical likelihood-based confidence intervals for data containing many zero observations
    Patrick Stewart
    Wei Ning
    [J]. Computational Statistics, 2020, 35 : 2019 - 2042
  • [8] Modified empirical likelihood-based confidence intervals for data containing many zero observations
    Stewart, Patrick
    Ning, Wei
    [J]. COMPUTATIONAL STATISTICS, 2020, 35 (04) : 2019 - 2042
  • [9] Confidence intervals for the mean of a population containing many zero values under unequal-probability sampling
    Chen, Hanfeng
    Chen, Jiahua
    Chen, Shun-Yi
    [J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2010, 38 (04): : 582 - 597